English  |  正體中文  |  简体中文  |  Post-Print筆數 : 20 |  Items with full text/Total items : 90029/119959 (75%)
Visitors : 24034444      Online Users : 130
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/12612


    Title: An Intelligent Web-Page Classifier with Fair Feature-Subset Selection
    Authors: 陳志銘
    Chen, Chih-Ming;Lee, Hahn-Ming;Tan, Chia-Chen
    Keywords: Feature selection;Web page classification;Machine learning
    Date: 2006-12
    Issue Date: 2008-12-05 11:59:36 (UTC+8)
    Abstract: 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.
    Relation: Engineering Applications of Artificial Intelligence, 19(18), 967-978
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1016/j.engappai.2006.02.001
    DOI: 10.1016/j.engappai.2006.02.001
    Appears in Collections:[圖書資訊與檔案學研究所] 期刊論文

    Files in This Item:

    File Description SizeFormat
    lias_001.pdf792KbAdobe PDF1563View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback