A Fuzzy Classification Based on Feature Selection for Web Pages
出版社：
- 会议名称：The 2004 IEEE/WIC/ACM International Conference on Web intelligence
- 举办地点：Beijing,China
- 举办日期：September 20-24，2004
- 页数：469-472
摘要内容：
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 cik 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 ?ikneed 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.
