The number of Internet users and the number of web pages being added to www increase dramatically every day. It is therefore required to automatically and efficiently classify web pages into web directories. This help...The number of Internet users and the number of web pages being added to www increase dramatically every day. It is therefore required to automatically and efficiently classify web pages into web directories. This helps the search engines to provide users with relevant and quick retrieval results. As web pages are represented by thousands of features, feature selection helps the web page classifiers to resolve this large scale dimensionality problem. This paper proposes a new feature selection method using Ward’s minimum variance measure. This measure is first used to identify clusters of redundant features in a web page. In each cluster, the best representative features are retained and the others are eliminated. Removing such redundant features helps in minimizing the resource utilization during classification. The proposed method of feature selection is compared with other common feature selection methods. Experiments done on a benchmark data set, namely WebKB show that the proposed method performs better than most of the other feature selection methods in terms of reducing the number of features and the classifier modeling time.展开更多
文摘The number of Internet users and the number of web pages being added to www increase dramatically every day. It is therefore required to automatically and efficiently classify web pages into web directories. This helps the search engines to provide users with relevant and quick retrieval results. As web pages are represented by thousands of features, feature selection helps the web page classifiers to resolve this large scale dimensionality problem. This paper proposes a new feature selection method using Ward’s minimum variance measure. This measure is first used to identify clusters of redundant features in a web page. In each cluster, the best representative features are retained and the others are eliminated. Removing such redundant features helps in minimizing the resource utilization during classification. The proposed method of feature selection is compared with other common feature selection methods. Experiments done on a benchmark data set, namely WebKB show that the proposed method performs better than most of the other feature selection methods in terms of reducing the number of features and the classifier modeling time.
