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題名 An Intelligent Web-Page Classifier with Fair Feature-Subset Selection
作者 陳志銘
Chen, Chih-Ming ;
     Lee, Hahn-Ming ;
     Tan, Chia-Chen
關鍵詞 Feature selection;
     Web page classification;
     Machine learning
日期 2006-12
上傳時間 5-Dec-2008 11:59:36 (UTC+8)
摘要 The explosion of on-line information has given rise to many manually constructed topic hierarchies (such as Yahoo!!). But with the current growth rate in the amount of information, manual classification in topic hierarchies results in an immense information bottleneck. Therefore, developing an automatic classifier is an urgent need. However, classifiers suffer from enormous dimensionality, since the dimensionality is determined by the number of distinct keywords in a document corpus. More seriously, most classifiers are either working slowly or they are constructed subjectively without any learning ability. In this paper, we address these problems with a fair feature-subset selection (FFSS) algorithm and an adaptive fuzzy learning network (AFLN) for classification. The FFSS algorithm is used to reduce the enormous dimensionality. It not only gives fair treatment to each category but also has ability to identify useful features, including both positive and negative features. On the other hand, the AFLN provides extremely fast learning ability to model the uncertain behavior for classification so as to correct the fuzzy matrix automatically. Experimental results show that both FFSS algorithm and the AFLN lead to a significant improvement in document classification, compared to alternative approaches.
關聯 Engineering Applications of Artificial Intelligence, 19(18), 967-978
資料類型 article
DOI http://dx.doi.org/10.1016/j.engappai.2006.02.001
dc.creator (作者) 陳志銘zh_TW
dc.creator (作者) Chen, Chih-Ming ;
     Lee, Hahn-Ming ;
     Tan, Chia-Chen
-
dc.date (日期) 2006-12en_US
dc.date.accessioned 5-Dec-2008 11:59:36 (UTC+8)-
dc.date.available 5-Dec-2008 11:59:36 (UTC+8)-
dc.date.issued (上傳時間) 5-Dec-2008 11:59:36 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/12612-
dc.description.abstract (摘要) The explosion of on-line information has given rise to many manually constructed topic hierarchies (such as Yahoo!!). But with the current growth rate in the amount of information, manual classification in topic hierarchies results in an immense information bottleneck. Therefore, developing an automatic classifier is an urgent need. However, classifiers suffer from enormous dimensionality, since the dimensionality is determined by the number of distinct keywords in a document corpus. More seriously, most classifiers are either working slowly or they are constructed subjectively without any learning ability. In this paper, we address these problems with a fair feature-subset selection (FFSS) algorithm and an adaptive fuzzy learning network (AFLN) for classification. The FFSS algorithm is used to reduce the enormous dimensionality. It not only gives fair treatment to each category but also has ability to identify useful features, including both positive and negative features. On the other hand, the AFLN provides extremely fast learning ability to model the uncertain behavior for classification so as to correct the fuzzy matrix automatically. Experimental results show that both FFSS algorithm and the AFLN lead to a significant improvement in document classification, compared to alternative approaches.-
dc.format application/en_US
dc.language enen_US
dc.language en-USen_US
dc.language.iso en_US-
dc.relation (關聯) Engineering Applications of Artificial Intelligence, 19(18), 967-978en_US
dc.subject (關鍵詞) Feature selection;
     Web page classification;
     Machine learning
-
dc.title (題名) An Intelligent Web-Page Classifier with Fair Feature-Subset Selectionen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1016/j.engappai.2006.02.001en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1016/j.engappai.2006.02.001en_US