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for categorization under any child categories of ci . Document dj is propagated to all child nodes of ci if the decision is positive; otherwise, no propagation takes place. An expert network for ci simply decides whether document dj should be classi ed to cj . A classi cation tree consisting of gating or expert networks is used for document classi cation; leaf nodes are expert networks and internal nodes may be gating or expert. This allows a document to be classi ed under internal nodes and leaf nodes simultaneously.
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11.4.2 Overview
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There is usually much more information contained in a web document than a bookmark. Additionally information can be structured within a Web page that may indicate a relatively higher or lower importance of the contained text. For example, terms appearing within a <TITLE> tag would be expected to be more informative than the majority of those appearing within the document body at large. Because of this, keywords are weighted not only according to their statistical occurrence but also to their location within the document itself. These weights are almost always real valued, which can be a problem for most feature selectors unless data discretization takes place (a source of information loss). This motivates the application of FRFS to this domain. The training and testing datasets were generated using Yahoo [393]. Five classi cation categories were used, namely Art & Humanity, Entertainment, Computers & Internet, Health, and Business & Economy. A total of 280 Web sites were collected from Yahoo categories and classi ed into these categories, 56 sites per category resulting in a balanced dataset. From this collection of data, the keywords, weights and corresponding classi cations were collated into a single dataset (see Figure 11.3).
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11.4.3 Results
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11.4.3.1 Comparison with RSAR For this set of experiments FRFS is compared with the crisp RSAR approach. As the unreduced dataset exhibits high
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APPLICATIONS II: WEB CONTENT CATEGORIZATION
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dimensionality (2557 attributes), it is too large to evaluate (hence the need for keyword selection). Using crisp RSAR the original attribute set was reduced to 29 (1.13% of the full set of attributes). However, using FRFS the number of selected attributes was only 23 (0.90% of the full attribute set). It is interesting that the FRFS reduct and crisp RSAR reduct share four attributes in common. With such a large reduction in attributes, it must be shown that classi cation accuracy does not suffer in the FRFS-reduced system. In addition to the classi cation accuracy the precision of the system is presented. Precision is de ned here to be the ratio of the number of correctly classi ed documents to the total number of correctly and incorrectly classi ed documents (expressed as a percentage). This differs from the classi cation accuracy in that the accuracy also considers documents that have not been able to be classi ed by the system. To see the effect of dimensionality reduction, the system was tested on the original training data rst; the results are summarized in Table 11.3. The results are averaged over all the classi cation categories. Clearly, the fuzzy method exhibits better precision and accuracy rates. This performance was achieved using fewer attributes than the crisp approach. Table 11.4 contains the results for experimentation on 140 previously unseen Web sites. For the crisp case the average precision and accuracy are both rather low. With FRFS there is a signi cant improvement in both the precision and classi cation accuracy. Again, this more accurate performance is achieved while using fewer attributes. It must be pointed out that although the testing accuracy is rather low, this is largely to do with the poor performance of the simple classi ers used. The fact that VSM-based results are much better than those using BIM-based classi ers shows that when a more accurate classi cation system is employed, the accuracy can be considerably improved with the involvement of the same attributes. Nevertheless, the purpose of the present experimental studies is to compare the performance of the two attribute reduction techniques based on the common use of any given classi er. Thus only the relative accuracies are important. Also the FRFS approach requires a reasonable fuzzi cation of the input data, while the fuzzy sets are herein generated by simple statistical analysis of the dataset with no attempt made at optimizing these sets. A ne-tuned fuzzi cation will certainly improve the performance of FRFS-based systems [224].
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TABLE 11.4 Performance: unseen data Method % Original Classi er Attributes Crisp 1.13 BIM VSM Fuzzy 0.90 BIM VSM
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Average Precision 23.3% 45.2% 16.7% 74.9%
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Average Accuracy 11.7% 50.3% 20.0% 64.1%
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