| { | |
| "paper_id": "O08-6001", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T08:02:05.635056Z" | |
| }, | |
| "title": "Feature Weighting Random Forest for Detection of Hidden Web Search Interfaces", | |
| "authors": [ | |
| { | |
| "first": "Yunming", | |
| "middle": [], | |
| "last": "Ye", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Harbin Institute of Technology", | |
| "location": { | |
| "country": "China" | |
| } | |
| }, | |
| "email": "yeyunming@hit.edu.cn" | |
| }, | |
| { | |
| "first": "Hongbo", | |
| "middle": [], | |
| "last": "Li", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Harbin Institute of Technology", | |
| "location": { | |
| "country": "China" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Xiaobai", | |
| "middle": [], | |
| "last": "Deng", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Harbin Institute of Technology", | |
| "location": { | |
| "country": "China" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Joshua", | |
| "middle": [ | |
| "Zhexue" | |
| ], | |
| "last": "Huang", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "jhuang@eti.hku.hk" | |
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| "year": "", | |
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| "identifiers": {}, | |
| "abstract": "Search interface detection is an essential task for extracting information from the hidden Web. The challenge for this task is that search interface data is represented in high-dimensional and sparse features with many missing values. This paper presents a new multi-classifier ensemble approach to solving this problem. In this approach, we have extended the random forest algorithm with a weighted feature selection method to build the individual classifiers. With this improved random forest algorithm (IRFA), each classifier can be learned from a weighted subset of the feature space so that the ensemble of decision trees can fully exploit the useful features of search interface patterns. We have compared our ensemble approach with other well-known classification algorithms, such as SVM, C4.5, Na\u00efve Bayes, and original random forest algorithm (RFA). The experimental results have shown that our method is more effective in detecting search interfaces of the hidden Web.", | |
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| "abstract": [ | |
| { | |
| "text": "Search interface detection is an essential task for extracting information from the hidden Web. The challenge for this task is that search interface data is represented in high-dimensional and sparse features with many missing values. This paper presents a new multi-classifier ensemble approach to solving this problem. In this approach, we have extended the random forest algorithm with a weighted feature selection method to build the individual classifiers. With this improved random forest algorithm (IRFA), each classifier can be learned from a weighted subset of the feature space so that the ensemble of decision trees can fully exploit the useful features of search interface patterns. We have compared our ensemble approach with other well-known classification algorithms, such as SVM, C4.5, Na\u00efve Bayes, and original random forest algorithm (RFA). The experimental results have shown that our method is more effective in detecting search interfaces of the hidden Web.", | |
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| "section": "Abstract", | |
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| "text": "Hidden Web refers to the Web pages that are dynamically generated from searchable structured or unstructured databases. Different from the Publicly Indexable Web that is accessible through static hyperlinks, the pages in a hidden Web can only be obtained through queries submitted via the search interface to the databases containing data about the hidden Web. Search interfaces are usually encoded as HTML forms that need to be filled out and submitted by users to obtain information. On the Web, there are many different HTML forms, and many of them are not search interfaces (He, Patel, Zhang, & Chang, 2007) . To extract useful information from hidden Web pages, effectively detecting the search interfaces is an essential step since the interface is the only entrance to the hidden Web. Therefore, we first need to find the entrance to the hidden database. The entrance (i.e., search forms) is mixed with lots of non-search forms in HTML pages. Thus, it is very important to distinguish the two types of forms in order to enable the Hidden Web crawler to locate the entrance and extract information further.", | |
| "cite_spans": [ | |
| { | |
| "start": 578, | |
| "end": 611, | |
| "text": "(He, Patel, Zhang, & Chang, 2007)", | |
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| "section": "Introduction", | |
| "sec_num": "1." | |
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| "text": "Information extraction from the hidden Web has been a hot research topic (BrightPlanet.com, 2000) since the term \"Hidden Web\" was first coined (Florescu, Levy, & Mendelzon, 1998) . Most previous work, however, has been focused on the problems of automatic query generation (Ntoulas, Zerfos, & Cho, 2005) , form filling (Cavelee, Liu, & Probe, 2004) , and wrapper generation for extracting structured information (Wang & Lochovsky, 2003) , where detecting search interface was performed manually or by some heuristic methods. Using heuristic rules to find search forms is the simplest and most effective method (Raghavan & Garcia-Molina, 2001; Lage, Silva, Golgher, & Laender, 2004) . As hidden Web sites adopt different search forms, though, it is time-consuming to compose different rules for different search forms. Employing machine learning and information retrieval techniques to learn classification models from the content of search forms and using the models to classify different forms automatically is a more desirable approach with respect to scalability and robustness. This approach regards search interface detection as a two-class classification problem. One example of this is using decision trees to build form classification models to detect search interface (Cope, Craswell, & Hawking, 2003) .", | |
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| "text": "(BrightPlanet.com, 2000)", | |
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| "end": 178, | |
| "text": "(Florescu, Levy, & Mendelzon, 1998)", | |
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| "end": 303, | |
| "text": "(Ntoulas, Zerfos, & Cho, 2005)", | |
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| "end": 348, | |
| "text": "(Cavelee, Liu, & Probe, 2004)", | |
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| "start": 412, | |
| "end": 436, | |
| "text": "(Wang & Lochovsky, 2003)", | |
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| "start": 610, | |
| "end": 642, | |
| "text": "(Raghavan & Garcia-Molina, 2001;", | |
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| "end": 681, | |
| "text": "Lage, Silva, Golgher, & Laender, 2004)", | |
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| "section": "Introduction", | |
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| "text": "Automatic search interface detection was first explored by Raghavan and Garcia-Molina in their hidden Web crawler HiWE (Hidden Web Exposer) (Raghavan & Garcia-Molina, 2001 ). Their crawling system used heuristic rules to detect the search entrance to the hidden database. Juliano P. Lage (Lage, Silva, Golgher, & Laender, 2004) used two heuristic rules to perform detection tasks. The first heuristic was the same as HiWE. The second one was to check whether the form contains the \"password\" HTML element or not. The disadvantage of this method, however, lies in that it does not have auto-leaning capability. Moreover, it is not robust and scalable to diverse hidden Web databases because the rules are too simple to match different form structures. Cope et al. (2003) used a decision tree classification algorithm to detect search interfaces. This method usually generates long rules due to the large size of the feature space in the training set (the number of training samples is too small compared to the number of features). Therefore, it is prone to overfitting, and the classification precision is not satisfying. Zhang et al. (2004) presented a best-effort parsing framework to address the problem of understanding Web search interfaces. The authors transformed search interfaces into a visual language under the hypothesis that automatic construction of search interfaces is guided by a hidden syntax. This hypothesis enables parsing as a principled framework to understand the semantic model of the visual language. The experimental results testified the effectiveness of their approach.", | |
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| "section": "Introduction", | |
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| "text": "To summarize, little previous work has addressed the special characteristics of the search interface detection domain, for instance, large and diverse features, small size of training samples with many missing values, etc. The high dimension and sparse data of search interfaces present a tricky problem for the traditional single classifier approach. As collecting training samples (i.e., HTML forms) is costly, the training data set is usually small, while the number of features in the learning space is relatively large, due to multi-type features existing in forms. It's difficult for a single classifier to fully exploit the rich feature space (very sparse in the data matrix). For many classification methods, the single classifier tends to be overfitting. To attack this problem, we propose a multi-classifier ensemble approach in this paper.", | |
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| "text": "Our method is based on the random forest algorithm. A random forest model consists of a set of decision trees that are constructed by bootstrapping the training data. In our approach, we develop a weighted feature selection method to select a subset of features for each decision tree in the tree induction process. Classification is made by aggregating predictions of individual decision trees in the forest. Since each classifier is learned from a subset of the feature space, the ensemble approach can fully exploit the useful features in search forms. We have conducted experiments on several real data sets. The experimental results have shown that our random forest approach improves the classification accuracy in search interface detection.", | |
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| "section": "Introduction", | |
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| "text": "The contributions of this paper can be summarized as follows:", | |
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| "section": "Introduction", | |
| "sec_num": "1." | |
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| "text": "1. We explored the random forest approach to attacking the problem of detecting search interfaces from the sparse feature space of hidden Webs where specific feature extraction and representation techniques were used.", | |
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| "section": "Introduction", | |
| "sec_num": "1." | |
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| "text": "2. We extended the random forest algorithm with a weighted feature selection method to select a subset of features for each decision tree. The new algorithm can automatically remove the noisy features in search forms so that decision tree classifiers can be learned from more representative subsets of the feature space.", | |
| "cite_spans": [], | |
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| "section": "Introduction", | |
| "sec_num": "1." | |
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| "text": "3. We conducted experiments on real data sets to compare the improved random forest algorithm with other well-known classification algorithms, such as SVM and C4.5. The experimental results have shown that the new method is more effective in detecting search interfaces of the hidden Web.", | |
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| "section": "Introduction", | |
| "sec_num": "1." | |
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| "text": "The rest of this paper is organized as follows. In Section 2, we formalize the detecting search interface problem as a form classification problem and present the feature extraction techniques. Section 3 describes the improved random forest algorithm for form classification. Experimental results and analysis are presented in Section 4. Section 5 concludes this paper and presents our future work.", | |
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| "text": "Search interface detection is a process of distinguishing the search forms of the hidden Web from non-search forms. It is a two-class classification problem in machine learning. This section describes how to extract form features from HTML pages and discusses the characteristics of the data matrix for form classification. HTML forms contain two kinds of features: one is the attributes of forms and elements, and the other is the statistics of those attributes. A form mainly contains four kinds of elements, that is, \"INPUT\", \"SELECT\", \"LABEL\" and \"TEXTAREA\", which are the children elements of \"FORM\" element. Element \"INPUT\" contains several types, such as \"text\", \"hidden\", etc. The hierarchy of a form is shown in Figure 1 . All of these elements contain a set of attributes, such as \"name\", \"value\", \"size\", etc. The attributes of \"form\" elements are \"method\", \"action\", and \"name\". Attribute \"method\" indicates the method for the form to submit query data, such as \"POST\" or \"GET\". Attribute \"action\" indicates the address of the corresponding server of the form, and attribute \"name\" indicates the name of the form. Some elements and attributes can be removed because they are not useful for form classification, for instance \"option\", \"size\", \"width\", etc. Besides, the statistics about the number of elements or attributes in each element can also be computed as important features. Figure 2 shows an example of a search form. The form contains three elements: one \"SELECT\" element and two \"INPUT\" elements. There are three attributes in the \"SELECT\" element : \"name\" with value \"and\", \"size\" with value \"1\", and \"width\" with value \"50\". It also contains several \"OPTION\" elements. The \"size\" and \"width\" attributes, along with the \"OPTION\" elements are not useful for form classification, so they can be removed. The corresponding HTML codes are shown in Figure 3 . Figure 5 . According the feature extraction rules, the useful form elements in this form include \"INPUT\", \"LABEL\", and \"FORM\", which can be used to compose the feature space. Elements \"TABEL\", \"FONT\", and \"TR\" can be removed because they are not useful. There are some important differences between the features of search form and non-search form. First, the number of \"INPUT\", \"SELECT\", and \"TEXTAREA\" elements in search forms is larger than that in non-search forms. Second, the value of the \"method\" attribute in \"FORM\" elements is always set as \"POST\" in search forms, while it is always set as \"GET\" in non-search forms. Moreover, the elements' values of search forms often contain some keywords such as \"search\", \"find\", or other words that have the same meaning as \"search\". These differences, however, are not the only decisive factors. There are other features that can be explored by classification algorithms.", | |
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| "text": "Figure 1", | |
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| "end": 1403, | |
| "text": "Figure 2", | |
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| "end": 1876, | |
| "text": "Figure 3", | |
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| "text": "Figure 5", | |
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| "section": "Feature Extraction for Form Classification", | |
| "sec_num": "2." | |
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| { | |
| "text": "According to the major differences, six kinds of rules are used in the feature extraction process as follows:", | |
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| "section": "Feature Extraction Rules", | |
| "sec_num": "2.1" | |
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| "text": "1. Extract the \"name\" attribute values from \"input\", \"select\", \"textarea\", and \"label\" elements;", | |
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| "section": "Feature Extraction Rules", | |
| "sec_num": "2.1" | |
| }, | |
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| "text": "2. Extract the \"value\" attribute value from \"input\", \"textarea\", and \"label\" elements;", | |
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| "sec_num": "2.1" | |
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| "text": "3. Extract the \"name\" and \"method\" attribute values from \"form\" elements;", | |
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| "section": "Feature Extraction Rules", | |
| "sec_num": "2.1" | |
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| "text": "4. Extract the words that appear between slashes(/) in the \"action\" attributes of the \"form\" elements;", | |
| "cite_spans": [], | |
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| "eq_spans": [], | |
| "section": "Feature Extraction Rules", | |
| "sec_num": "2.1" | |
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| "text": "5. Extract the words that appear between slashes(/) in the \"src\" and \"alt\" attributes of the \"input-image\" element;", | |
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| "text": "6. Calculate the number of \"input\", \"select\", \"label\", and \"textarea\" elements in each form.", | |
| "cite_spans": [], | |
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| "section": "Feature Extraction Rules", | |
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| "text": "The next step is to standardize the value of the features that are extracted from the forms.", | |
| "cite_spans": [], | |
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| "section": "Feature Extraction Rules", | |
| "sec_num": "2.1" | |
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| { | |
| "text": "First, all strings are transformed into lowercases; then the string type values are aggregated and mapped to specific enumerating values. For instance, the values of \"search\", \"find\", or \"srch\" are mapped to \"search\".", | |
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| "section": "Feature Weighting Random Forest for Detection of 393 Hidden Web Search Interfaces", | |
| "sec_num": null | |
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| "text": "The extracted features and the labels of forms are used to compose the data matrix for the classification algorithm. The formalized data matrix is shown in Table 1 . ", | |
| "cite_spans": [], | |
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| { | |
| "start": 156, | |
| "end": 163, | |
| "text": "Table 1", | |
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| "section": "The Sparse Data Matrix", | |
| "sec_num": "2.2" | |
| }, | |
| { | |
| "text": "class 1 t 2 t i t m t 1 c 11 a 12 a 1i a 1m a j c 1 j a 2 j a ji a jm a n c 1 n a 2 n a ni a nm a The set { } 1 2 m T t t t = ,", | |
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| { | |
| "text": ", in Table 1 represents the names of the form features. For form classification, the label of a form is represented as an element in the set { } C yes no = , , while \"yes\" indicates that the form is a search interface of hidden Web and \"no\" indicates a non-search interface. Each row is a sample form. ji a represents the value of feature i t in the j th form, and j c indicates the class of the j th form. Table 2 illustrates two examples of a search form and a non-search form as shown in Figure 2 and Figure 4 .", | |
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| "text": "Figure 2", | |
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| "text": "Figure 4", | |
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| "sec_num": "2.2" | |
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| "text": "The expression of i t is a four-tuple of \"element name\"-\"type\"-\"attribute name\"-\"sequence number\". The \"element name\" contains six values: \"FORM\", \"SELECT\", \"INPUT\", \"TEXT AREA\", \"LABEL\", and their statistics. For element, \"INPUT\", the value of \"type\" can be \"text\", \"hidden\", and so on. \"attribute name\" has six options: \"name\", \"value\", \"src\", \"alt\", \"method\", and \"action\". Sequence number represents the sequence of the features in the form.", | |
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| "text": "As illustrated in Table 2 , the combination of \"element name\",\"type\",\"attribute name\", and \"sequence number\" has many unique alternatives. This will result in a high-dimensional feature space for form classification. Furthermore, since each form has just a few features, the data matrix for classification is very sparse and there are many missing values and noisy features. This problem presents a big challenge for search form detection. ", | |
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| "sec_num": "2.2" | |
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| "text": "This section presents an improved random forest algorithm, which extends the classical random forest method with a feature weighting technique. We describe the basic random forest classification approach in Subsection 3.1 and our new algorithm in Subsection 3.2.", | |
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| "section": "Feature Weighting Random Forest Algorithm", | |
| "sec_num": "3." | |
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| { | |
| "text": "Random Forest (RFA) (Ho, 1998; Breiman, 2001 ) is an ensemble of unpruned classification or regression trees, which is induced from bootstrapping samples of the training set, using random feature selection in the tree induction process. Prediction is made by aggregating the predictions of the ensemble. Random Forest grows many classification trees. To classify a new object from an input vector, it passes the sample vector to each of the trees in the forest. Each tree gives a classification decision. All the classification results of individual trees are combined to choose the classification having the most votes over all the classification trees in the forest.", | |
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| "text": "Random forest generally exhibits a substantial performance improvement over single tree classifiers, such as CART (Breiman, Friedman, & Olshen, 1984) and C4.5 (Quinlan, 1993) . It presents a good solution for classification of sparse data sets. Since basic RFA selects features randomly, it's easy to select unimportant or noisy features, especially when there are many noisy features in the training data. This may lead to bad classification results. As discussed in previous sections, the data matrix for form classification contains many missing values. It's necessary to enhance basic RFA so that the performance can be improved in search form classification.", | |
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| "text": "(Quinlan, 1993)", | |
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| } | |
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| "section": "Random Forest Algorithm", | |
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| "text": "Due to the sparse feature space, there are a lot of missing values in the training data set. The features with too many missing values become less important and can be treated as noisy features. Random selection of features often obtains many unimportant or noisy features, which leads to bad trees in the forest. To avoid this, we extend basic RFA, using a weighting scheme in feature selection to replace random selection. We use 2 \u03c7 statistic to measure the importance of features (Larson, 1982) . The 2 \u03c7 statistic of a feature A against the class feature is computed as follows. ", | |
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| "start": 484, | |
| "end": 498, | |
| "text": "(Larson, 1982)", | |
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| "section": "Improved Random Forest with Weighted Feature Selection", | |
| "sec_num": "3.2" | |
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| "text": "If class i c has the highest probability, i c is the class of x . Kittler has provided a more profound explanation of this method (Kittler, Hatef, Duin, & Matas, 1998) . The pseudo-code of the new algorithm (IRFA) is given in 1 Algorithm .", | |
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| "start": 130, | |
| "end": 167, | |
| "text": "(Kittler, Hatef, Duin, & Matas, 1998)", | |
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| "text": "Step 1 is to compute a weight for each feature according to (1). Step 2 sorts the features in descending order of feature weights. Step 3 selects n\u2032 features from the entire feature set according to a given feature selection rate \u03b2 .", | |
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| "text": "Step 4 learns individual classifiers from the selected training samples (and selected features). Selection of training samples employs the bootstrapping method. The method of sampling without replacement is used to select t features from n\u2032 features, where 2 log 1 t n = + \u23a2 \u23a5 \u23a3 \u23a6 . After each iteration, the learned decision tree classifier will be added to forest M * . After forest M * is grown,", | |
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| "text": "Step 5 classifies the unlabeled instances based on (4).", | |
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| "section": "Improved Random Forest with Weighted Feature Selection", | |
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| "text": "The pseudo-code of the feature weighting random forest algorithm Input: ", | |
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| "text": "The computational complexity of RFA (Breiman, 2001) The computational cost depends on three factors: the number of decision trees k , the number of features n , and the number of training samples d . We will discuss how to select the number of decision trees k and the number of features n to balance between classification accuracy and computational cost in Section 4. ", | |
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| "section": "The computational complexity", | |
| "sec_num": "3.3" | |
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| "text": "We used two Web page collections in our experiments. One was taken from project Metaquerier 1 , and the other was created by crawling the website Search Engine Guide 2 with a Web crawler implemented in Java. The two collections represent a pseudo-random crawling of the Web. A HTML parser was developed to extract the HTML forms and their context features from these two collections. The extracted forms were used to compose the final data sets for experiments. The forms are selected from data set 1 and data set 2.", | |
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| "section": "Data Sets", | |
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| "text": "We manually classified the extracted forms into search forms (i.e., real search interface of hidden Web) and non-search forms. Three classification data sets were constructed from the classified forms, as shown in Table 3 . The three data sets have a variety of sample distributions. Data set 1 and Data set 2 cover different domains, while Data set 3 is a mixture of the two domains. The three data sets also have different feature types and feature numbers. The average number of features in the data sets is over 200, while the average number of samples is less than 140. The matrices of the three data sets were quite sparse, and the number of features was quite large.", | |
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| "section": "Figure 6. The domain distribution of non-search forms", | |
| "sec_num": null | |
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| "text": "To further test the robustness of our method, the non-search forms in the data sets were made of a variety of forms, including registration forms, login forms, network investigation forms, etc. Figure 6 shows the distribution of different non-search forms.", | |
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| "section": "Figure 6. The domain distribution of non-search forms", | |
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| "text": "We first carried out experiments to compare our random forest method with four well-known classification algorithms, i.e., Support Vector Machine (SVM), C4.5, Na\u00efve Bayes,and Random Forest Algorithm (RFA) implemented in Weka 3 . We also implemented our algorithm (IRFA) as a plug-in of Weka and conducted all experiments in this environment to make a fair comparison. We conducted the standard 10-fold classification experiments on the three data sets. The evaluation metrics used were precision and computation time. The number of trees was set to 100 for IRFA and parameter \u03b2 set to 0.5 . The final experimental results are shown in Table 4 . We can see that IRFA showed significant improvement over the other four algorithms. The result of C4.5 was better than SVM and Na\u00efve Bayes. This was due to the fact that there were a lot of missing values in the data sets and SVM and Na\u00efve Bayes did not perform well in this kind of sparse data. The high dimensionality in the training sets, however, causes an overfitting problem to C4.5 because the single decision tree could become very complex. RFA and IRFA can avoid this problem by selecting different subsets of features to build individual decision trees. Compared with RFA, IRFA uses features that are more correlated to the class label feature, so the accuracy of each individual tree is improved. Therefore, our method got better performance than RFA. Since IRFA needed to compute the 2 \u03c7 values for features, it", | |
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| "section": "Comparison Experiments", | |
| "sec_num": "4.2" | |
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| "text": "Hidden Web Search Interfaces took more computational time. This extra overhead, however, is worthwhile and acceptable in real applications because the training process is offline and not executed frequently.", | |
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| "text": "When building each decision tree, IRFA only selects a subset of the original features. The number of selected features is controlled by the selection rate \u03b2 . Different selection rates result in different classification precisions and computational costs. We carried out experiments on the three data sets with 0.1, 0.2, , 1.0", | |
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| "text": "\u03b2 = \u2026", | |
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| "text": ". Figure 7 plots selection rate \u03b2 against precision, while Figure 8 is \u03b2 against computational cost. Figure 7 shows that when 0.5 \u03b2 < , the precision increases greatly as the selection rate increases. The reason is that a larger selection rate increases the number of features to be selected. When 0.5 0.8 \u03b2 \u2264 \u2264 , the classification performance becomes relatively stable. This means that the forest has selected enough discriminative features. When 0.8 \u03b2 > , the precision will decrease as the selection rate increases. This can be explained by the idea that having too large a selection rate will increase the possibility of selecting noisy features. Most experiments have shown that 0.5 \u03b2 = was a good setting. Figure 8 shows that the computational time of IRFA increases linearly as the feature selection rate increases. This property indicates that IRFA is scalable to large high-dimensional data. ", | |
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| "text": "Another interesting issue is whether the performance of IRFA highly depends on the size of a forest (number of decision trees in the forest). Since a large number of trees lead to a considerable computational cost, we need to find a good tradeoff between classification precision and computational cost. We performed experiments on the three data sets with different tree numbers ( 10, 25, 50, 75, 100, 125, 150, 200) k = . The feature selection rate \u03b2 was set to 0.5 in all experiments. The experimental results are shown in Figure 9 and Figure 10 . Figure 9 plots the precision against the number of trees k . The results show that if the number of trees is too small, the classification performance of the forest will be unstable. If the number of trees is too large, however, the computational cost for generating a forest will be very high. The classification precision becomes stable when 75 150 k < <", | |
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| "text": ". The near-optimal precision can be obtained when k is set to 100. Moreover, as shown in Figure 10 , with the increase of the number of trees, the computational time increases linearly as well. Therefore, in most situations, 100 k = is a good balance between classification accuracy and computational cost. ", | |
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| "text": "The representation of search forms in different domains varies, so it is necessary to investigate if the performance of IRFA can be consistent in different domains. An experiment was designed to classify search interfaces from four different domains. We used the Web collection provided by Metaquerier 4 , which contains the information about Books, Movies, Airfares, and Jobs. The number of trees for IRFA was set to 100, and parameter \u03b2 was set to 0.5.", | |
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| "section": "Comparison in Different Domains", | |
| "sec_num": "4.5" | |
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| "text": "The experimental results are shown in Table 5 . We can see the obvious variance of accuracy with regard to different domains. The detection accuracies for Books and Jobs domains are higher than those of Movies and Airfares. This was due to the different HTML structures of the search interfaces, as the search interfaces of Movies and Airfares domains are more complex and some of them require more than one step to get the content. The results imply that more formalized and simpler interfaces are more easily recognized. From the results, we also observe that the classification performance of IRFA was very stable in various domains. Even though the precision of SVM on the Movies data set was a little better than IRFA, it became the worst in other domains. Compared with other algorithms, IRFA was the most stable. ", | |
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| "section": "Comparison in Different Domains", | |
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| "text": "We conducted experiments to demonstrate the performance of IRFA with different feature selection schemes. In this section, we used four commonly used feature selection functions mentioned in (Dash & Liu, 1997) .", | |
| "cite_spans": [ | |
| { | |
| "start": 191, | |
| "end": 209, | |
| "text": "(Dash & Liu, 1997)", | |
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| "section": "Comparison with Different Feature Selection Schemes", | |
| "sec_num": "4.6" | |
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| { | |
| "text": "1. Random selection, which randomly selects the features using sampling without replacement, is the simplest feature selection method. Random feature selection is the method used in the original random forest (Breiman, 2001 ).", | |
| "cite_spans": [ | |
| { | |
| "start": 209, | |
| "end": 223, | |
| "text": "(Breiman, 2001", | |
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| "section": "Comparison with Different Feature Selection Schemes", | |
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| { | |
| "text": "2. Information gain is defined as the difference between the original information requirement and the new requirement (Han & Kamber, 2007) . The information gain value of a feature X is attained by computing the difference between the prior uncertainty and expected posterior uncertainty using X . Feature X is preferred to feature Y if the information gain from feature X is greater than that from feature Y (Dash & Liu, 1997) . In this experiment, the information gain from feature X is set to feature X as its weight instead of Chi-square value in IRFA.", | |
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| { | |
| "start": 118, | |
| "end": 138, | |
| "text": "(Han & Kamber, 2007)", | |
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| "start": 409, | |
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| "text": "(Dash & Liu, 1997)", | |
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| ], | |
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| "section": "Comparison with Different Feature Selection Schemes", | |
| "sec_num": "4.6" | |
| }, | |
| { | |
| "text": "3. Gain Ratio is an extension of information gain. It attempts to overcome the problem that the information gain measure is biased toward tests with many outcomes (i.e., it prefers to select attributes having a large number of values) (Han & Kamber, 2007) . The process of embedding gain ratio into IRFA is similar to information gain in our experiment.", | |
| "cite_spans": [ | |
| { | |
| "start": 235, | |
| "end": 255, | |
| "text": "(Han & Kamber, 2007)", | |
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| "section": "Comparison with Different Feature Selection Schemes", | |
| "sec_num": "4.6" | |
| }, | |
| { | |
| "text": "4. Chi-square 2 \u03c7 that has been explained in Section 3.2. The experiments in the prior sections were all based on this feature selection method. ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Comparison with Different Feature Selection Schemes", | |
| "sec_num": "4.6" | |
| }, | |
| { | |
| "text": "We implemented the four feature selection methods in Weka's random forest package, and carried out experiments on the data sets described in Section 4.1. The results are shown in Table 6 . From the results, we can see that the Chi-square method is better than the others. The reason that Information Gain and Gain Ratio scheme did not attain better performance can be explained as follows: since both of them use the same feature evaluation criterion in feature sampling and tree construction (for node splitting), the information of features cannot be fully exploited.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 179, | |
| "end": 186, | |
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| ], | |
| "eq_spans": [], | |
| "section": "93.88%", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "This paper has proposed IRFA, an improved random forest algorithm, for detecting search interfaces of the hidden Web. We extend the original random forest algorithm with a weighted feature selection method to automatically select a more representative subset of features for building each decision tree. The new method can overcome the problem of classifying high-dimensional and sparse search interface data through the ensemble of decision trees, each learned from a different subset of the original feature space. We have implemented the new algorithm and compared it with SVM, C4.5, Na\u00efve Bayes, and original random forest algorithm (RFA). The experimental results have shown that our method is more accurate and robust. We have also observed that the new method is scalable to high dimensional data.", | |
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| "section": "Conclusions", | |
| "sec_num": "5." | |
| }, | |
| { | |
| "text": "In the future, we plan to investigate more feature weighting methods for construction of random forests. Currently, we just use the features in the search forms. It is expected that using contextual information near the search forms may improve detection performance.", | |
| "cite_spans": [], | |
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| "section": "Feature Weighting Random Forest for Detection of 403 Hidden Web Search Interfaces", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "http://metaquerier.cs.uiuc.edu/repository/ training data set 2 http://www.searchengineguide.com", | |
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| "text": "http://www.cs.waikato.ac.nz/ml/weka/", | |
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| "text": "http://metaquerier.cs.uiuc.edu/repository/training data set", | |
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| "back_matter": [ | |
| { | |
| "text": "This research is supported in part by NSFC under grant No.60603066 and China National High-tech Program under grants No.2007AA01Z436 and No.2006AA01A124. Part of Joshua Huang's research was supported by the 863 project matching fund from The University of Hong Kong.", | |
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| "section": "Acknowledgements", | |
| "sec_num": null | |
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| "text": "A search formFigure 3. The HTML codes of the form inFigure 2", | |
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| "text": "shows an example of a non-search form. The corresponding HTML codes are shown in", | |
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| "text": "A non-search form", | |
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| "text": "The HTML codes of the form inFigure 4", | |
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| "text": "of samples whose value of the class feature is j c .An 2\u03c7 statistic weight is calculated for each feature. From the weights, we select only different subsets of features with high weights to build individual decision trees.Given a set of decision trees built from different subsets of features, we use a probability estimation technique to combine the results of individual classifiers. Assume x is a test instance and is given to each classifier (", | |
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| "text": "Influence of the number of features on precision Influence of the number of features on time cost 400YunmingYe et al.", | |
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| "text": "Influence of the number of trees on precision Figure 10. Influence of the number of trees on time cost Feature Weighting Random Forest for Detection of 401 Hidden Web Search Interfaces", | |
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| "content": "<table><tr><td colspan=\"4\">class form-action-1 form-action-2 form-action-3</td><td colspan=\"2\">input-text-number input-submit-number</td></tr><tr><td colspan=\"2\">yes www.thearda.com</td><td>cgi-bin</td><td>search</td><td>1</td><td>1</td></tr><tr><td>no</td><td>servlet</td><td>login</td><td>?</td><td>3</td><td>0</td></tr></table>", | |
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| "text": ", where k is the number of decision trees, t is the number of attributes, d is the number of training samples. In IRFA, the enumerating number of the feature attribute is constant(Formula (1)). The computational complexity of all feature weights is ( ) O n . Using the bucket sorting method, the weights can be sorted in linear time. Therefore, the computational complexity of the IRFA", | |
| "content": "<table><tr><td>is ( log ) O ktd d is ( log ) O ktd d n \u03b1 + , where 2 log 1 t n = + \u23a2 \u23a5 \u23a3 \u23a6 . Therefore, the computational complexity of</td></tr><tr><td>IRFA is very close to the complexity of RFA.</td></tr></table>", | |
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| "content": "<table><tr><td/><td>search</td><td>non-search</td><td colspan=\"2\">features content of data sets</td></tr><tr><td/><td/><td/><td/><td>Extracted from the website</td></tr><tr><td>Data set 1</td><td>46</td><td>43</td><td>198</td><td>collection crawled from Search</td></tr><tr><td/><td/><td/><td/><td>Engine Guide by crawler</td></tr><tr><td/><td/><td/><td/><td>Extracted from artificial</td></tr><tr><td>Data Set 2</td><td>65</td><td>116</td><td>208</td><td>website collection of</td></tr><tr><td/><td/><td/><td/><td>Metaquerier project</td></tr><tr><td>Data Set 3</td><td>51</td><td>96</td><td>202</td><td/></tr></table>", | |
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| "content": "<table><tr><td/><td/><td colspan=\"3\">Bayes C4.5 Decision Tree SVM</td><td>RFA</td><td>IRFA</td></tr><tr><td>Data Set 1</td><td>Precision Time Cost(s)</td><td>82.02% < 0.005</td><td>80.90% 0.05</td><td>79.78% 0.52</td><td>88.76% 3.16</td><td>91.01% 7.08</td></tr><tr><td>Data Set 2</td><td>Precision Time Cost(s)</td><td>83.43% < 0.005</td><td>88.40% 0.03</td><td>82.87% 0.88</td><td>91.71% 5.22</td><td>92.27% 17.86</td></tr><tr><td>Data Set 3</td><td>Precision Time Cost(s)</td><td>84.35% < 0.005</td><td>89.79% 0.02</td><td>85.71% 0.88</td><td>91.84% 3.58</td><td>93.88% 15.13</td></tr></table>", | |
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| "content": "<table><tr><td/><td colspan=\"2\">Dataset Size Bayes</td><td>C4.5</td><td>SVM</td><td>RFA</td><td>IRFA</td></tr><tr><td>Books</td><td>251</td><td colspan=\"3\">92.83% 96.41% 89.24%</td><td>92.03% 96.81%</td></tr><tr><td>Movies</td><td>126</td><td colspan=\"4\">91.27% 91.27% 92.06% 87.30%</td><td>91.27%</td></tr><tr><td>Airfares</td><td>265</td><td colspan=\"3\">89.06% 90.94% 87.92%</td><td>91.69% 91.70%</td></tr><tr><td>Jobs</td><td>104</td><td colspan=\"4\">88.46% 94.23% 78.85% 96.15% 96.15%</td></tr></table>", | |
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| "content": "<table><tr><td/><td>Random</td><td colspan=\"3\">Information Gain Gain Ratio Chi-square 2 \u03c7</td></tr><tr><td>Data Set 1</td><td>88.76%</td><td>88.76%</td><td>89.89%</td><td>91.01%</td></tr><tr><td>Data Set 2</td><td>91.71%</td><td>91.16%</td><td>92.27%</td><td>92.27%</td></tr><tr><td>Data Set 3</td><td>91.84%</td><td>93.20%</td><td>93.20%</td><td/></tr></table>", | |
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