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The World Wide Web (WWW) is an information resource whose full potential may not be realized unless its content is adequately organized and described. This not only applies to the vast network of Web pages but also to users personal repositories of Web page bookmarks. However, due to the immense size and dynamicity of the Web, manual categorization is not a practical solution to this problem. There is a clear need for automated classi cation of Web content. To demonstrate the applicability of the described fuzzy-rough methods, two relevant domains of interest regarding the Web were selected, namely bookmark classi cation and Web site classi cation. However, these domains are quite distinct, possessing features and problems that must be independently addressed. This section will initially present those features that are common to both before focusing on domain-dependent issues. Both applications employ a similar general system architecture in order to reduce dimensionality and perform categorization. A key issue in the design of the system was that of modularity; it should be modeled in such a way as to enable the straightforward replacement of existing techniques with new methods. The current implementations allow this exibility by dividing the overall process into several independent submodules (see Figure 11.1):
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Splitting of training and testing datasets. Datasets were generated from large textual corpora and separated randomly into training and testing sets. Each dataset is a collection of documents, either bookmarks or Web pages depending on the application. Keyword acquisition. Given the output from the previous module, keywords are extracted and weighted according to their perceived importance in the document, resulting in a new dataset of weight-term pairs. Note that in this work, no sophisticated keyword acquisition techniques are used as the current focus of attention is on the evaluation of attribute reduction. However, the use of more effective keyword acquisition techniques recently built in the area of information retrieval would help improve the system s overall classi cation performance further. Keyword selection. As the newly generated datasets can be too large, mainly due to keyword redundancy, to perform classi cation at this stage, a dimensionality reduction step is carried out using FRFS. If this step is preceded by a discretization phase, RSAR may also be applied to the data. Keyword ltering. Used only in testing, this simple module lters the keywords obtained during acquisition, using the reduct generated in the keyword selection module. Classi cation. This nal module uses the reduced dataset to perform the actual categorization of the test data. More ef cient and effective classi ers can be employed for this step, but for simplicity only conventional classi ers are adopted here to show the power of attribute reduction. Better
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APPLICATIONS II: WEB CONTENT CATEGORIZATION
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Figure 11.1 Modular decomposition of the classi cation system
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classi ers are expected to produce more accurate results, although not necessarily to enhance the comparisons between classi ers that use reduced or unreduced datasets.
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BOOKMARK CLASSIFICATION
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As the use of the World Wide Web becomes more prevalent and the size of personal repositories grows, adequately organizing and managing bookmarks becomes crucial. Several years ago, in recognition of this problem, web browsers included support for tree-like folder structures for organizing bookmarks. These enable the user to browse through their repository to nd the necessary information. However manual uniform resource locater (URL) classi cation
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and organization can be dif cult and tedious when there are more than a few dozen bookmarks to classify something that goes against the whole grain of the bookmarking concept. Many usability studies (e.g., [195]) indicate that a deep hierarchy results in less ef cient information retrieval as many traversal steps are required, so users are more likely to make mistakes. Users also do not have the time and patience to arrange their collection into a well-ordered hierarchy. The present work can help extract information from this relatively information-poor domain in order to classify bookmarks automatically.
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