English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 62797/95867 (66%)
造訪人次 : 3730212      線上人數 : 661
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/108550


    題名: Spam E-mail Classification Based on the IFWB Algorithm
    作者: Jou, Chi-Chang
    關鍵詞: spam classification;incremental forgetting;misclassification cost
    日期: 2013-03-19
    上傳時間: 2016-11-26 02:11:47 (UTC+8)
    出版者: Springer Berlin Heidelberg
    摘要: The problem of spam overflow has not been solved completely. Many anti-spam techniques have been proposed. Among them, the machine learning techniques are the most popular, but these works are based on a static environment assumption. In the real world application, the email context may change with concept drift. The classification result is usually good at the beginning, but along with time evolution and concept drift, the classification accuracy dropped down gradually. So a mechanism is needed to adjust the classifier according to the new incoming emails and the old emails in the dataset. Another problem of email categorization is data skewedness. Because of the spam overflow, the number of spam emails is far more than that of legitimate ones. In the classification result, the majority class is with higher recall rate, but the minority class with poor recall rate. For these reasons, we propose an algorithm, IFWB (Incremental Forgetting Weighted Bayesian), based on Naïve Bayesian and IGICF (Information Gain and Inverse Class Frequency) feature extraction, combined with gradual forgetting mechanism and cost-sensitive model to tackle concept drift and data skewedness. Finally, we demonstrate the effectiveness of the IFWB algorithm through a series of experiments.
    關聯: Lecture Notes in Artificial Intelligence 7802, pp.314-324
    DOI: 10.1007/978-3-642-36546-1_33
    顯示於類別:[資訊管理學系暨研究所] 期刊論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    index.html0KbHTML163檢視/開啟
    Spam E-mail Classification Based on the IFWB Algorithm(1).pdf502KbAdobe PDF1檢視/開啟

    在機構典藏中所有的資料項目都受到原著作權保護.

    TAIR相關文章

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