English  |  正體中文  |  简体中文  |  Items with full text/Total items : 51296/86412 (59%)
Visitors : 8175413      Online Users : 62
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU 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://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/101113


    Title: Spam E-mail Classification Based on the IFWB Algorithm
    Authors: Jou, Chichang
    Contributors: 淡江大學資訊管理學系
    Date: 2013-03-19
    Issue Date: 2015-04-13 16:21:08 (UTC+8)
    Publisher: Springer
    Abstract: The problem of spam e-mails has been addressed for some time. Most of the solutions are based on spam e-mail classification and filtering. However, the content of spam e-mails drifts with new concepts or social events. Thus, several spam classifiers perform effectively when their models are initially established, and their performances deteriorate with time. A learning mechanism is required to adjust the classification parameters for new and old e-mails. Because of the spread of spam e-mails, the number of spam e-mails is larger than that of legitimate e-mails. Therefore, most classifiers produce high recall for spam e-mails and low recall for legitimate e-mails. Based on the Bayesian algorithm, we propose an incremental forgetting weighted algorithm with a misclassification cost mechanism that extracts features by IGICF (Information Gain and Inverse Class Frequency) to address the problem of concept drift and data skew in spam e-mail classification. We implemented the algorithm and performed detailed tests on the effectiveness of the mechanism.
    Relation: Lecture Notes in Computer Science 7802, pp.314-324
    Appears in Collections:[資訊管理學系暨研究所] 會議論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML117View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback