Automatic text processing
Parallel computing (2nd ed.): theory and practice
Parallel computing (2nd ed.): theory and practice
Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
Unsupervised feature selection using a neuro-fuzzy approach
Pattern Recognition Letters
Classification of Web Documents Using a Graph Model
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
On-line incremental feature weighting in evolving fuzzy classifiers
Fuzzy Sets and Systems
A semantic matching of information segments for tolerating error chinese words
WISE'06 Proceedings of the 7th international conference on Web Information Systems
Automatic web pages hierarchical classification using dynamic domain ontologies
International Journal of Knowledge and Web Intelligence
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
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An automatic web page classification is needed for web information extraction, but the number of keywords of web pages is so giant that many classifications are not speedy or capable of self-learning. In this paper, a fuzzy classification method for web pages, which is based on fuzzy learning and parallel feature selection, is proposed. Fuzzy learning of parameter c{ik} is adopted to increase the accuracy, while parallel feature selection based on weighted similarity is used not only to decrease the dimension of the features but also to let parameter 驴{ik} need no learning. The weights of features are deducted in theory, and to speed up the calculation of weights, a parallel sum algorithm of the matrix is proposed.