Universiti Teknologi Malaysia Institutional Repository

Web page feature selection and classification using neural networks

Selamat, Ali and Omatu, Sigeru (2004) Web page feature selection and classification using neural networks. Information Sciences, 158 . pp. 69-88.

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Official URL: http://dx.doi.org/10.1016/j.ins.2003.03.003

Abstract

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. 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. 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.

Item Type:Article
Uncontrolled Keywords:text categorization, WWW, profile-based neural networks, principal component analysis
Subjects:Q Science > QA Mathematics > QA76 Computer software
Divisions:Computer Science and Information System (Formerly known)
ID Code:3095
Deposited By: Dr Ali Selamat
Deposited On:24 Oct 2007 08:23
Last Modified:01 Jun 2010 03:07

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