Selamat, Ali and Omatu, Sigeru and Yanagimoto, Hidekazu and Fujinaka, Toru and Yoshioka, Michifumi (2003) Web page classification method using neural networks. IEEJ Transactions on Electronics, Information and Systems, 123 (5). pp. 1020-1026.
Official URL: http://www2.iee.or.jp/~english/publish/cont2003.ht...
Automatic categorization is the only viable method to deal with the scaling problem of the World Wide Web (WWW). In this paper, we propose a news web page classification method (WPCM). The WPCM uses a neural network with inputs obtained by both the principal components and class profile-based features (CPBF). Each news web page is represented by the term-weighting scheme. As the number of unique words in the collection set is big, the principal component analysis (PCA) has been used to select the most relevant features for the classification. Then the final output of the PCA is combined with the feature vectors from the class-profile which contains the most regular words in each class before feeding them to the neural networks. We have manually selected the most regular words that exist in each class and weighted them using an entropy weighting scheme. The fixed number of regular words from each class will be used as a feature vectors together with the reduced principal components from the PCA. These feature vectors are then used as the input to the neural networks for classification. The experimental evaluation demonstrates that the WPCM method provides acceptable classification accuracy with the sports news datasets.
|Subjects:||Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources|
|Divisions:||Computer Science and Information System|
|Deposited By:||Dr Ali Selamat|
|Deposited On:||24 May 2007 08:04|
|Last Modified:||01 Jun 2010 03:07|
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