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    題名: An efficient incremental learning mechanism for tracking concept drift in spam filtering
    作者: 許志堅
    Sheu, Jyh‐Jian
    Chu, Ko-Tsung
    Li, Nien-Feng
    Lee, Cheng-Chi
    貢獻者: 傳播學院
    日期: 2017-02
    上傳時間: 2018-06-29 17:12:52 (UTC+8)
    摘要: This research manages in-depth analysis on the knowledge about spams and expects to propose an efficient spam filtering method with the ability of adapting to the dynamic environment. We focus on the analysis of email’s header and apply decision tree data mining technique to look for the association rules about spams. Then, we propose an efficient systematic filtering method based on these association rules. Our systematic method has the following major advantages: (1) Checking only the header sections of emails, which is different from those spam filtering methods at present that have to analyze fully the email’s content. Meanwhile, the email filtering accuracy is expected to be enhanced. (2) Regarding the solution to the problem of concept drift, we propose a window-based technique to estimate for the condition of concept drift for each unknown email, which will help our filtering method in recognizing the occurrence of spam. (3) We propose an incremental learning mechanism for our filtering method to strengthen the ability of adapting to the dynamic environment.
    關聯: PLOS ONE 【SCIE】, Vol.12, No.2, pp.e0171518
    資料類型: article
    DOI 連結: https://doi.org/10.1371/journal.pone.0171518
    DOI: 10.1371/journal.pone.0171518
    顯示於類別:[廣播電視學系] 期刊論文

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