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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 29-Jun-2018 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 https://doi.org/10.1371/journal.pone.0171518
dc.contributor 傳播學院
dc.creator (作者) 許志堅zh_TW
dc.creator (作者) Sheu, Jyh‐Jianen_US
dc.creator (作者) Chu, Ko-Tsungen_US
dc.creator (作者) Li, Nien-Fengen_US
dc.creator (作者) Lee, Cheng-Chien_US
dc.date (日期) 2017-02
dc.date.accessioned 29-Jun-2018 17:12:52 (UTC+8)-
dc.date.available 29-Jun-2018 17:12:52 (UTC+8)-
dc.date.issued (上傳時間) 29-Jun-2018 17:12:52 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/118158-
dc.description.abstract (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.en_US
dc.format.extent 1125791 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) PLOS ONE 【SCIE】, Vol.12, No.2, pp.e0171518
dc.title (題名) An efficient incremental learning mechanism for tracking concept drift in spam filteringen_US
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1371/journal.pone.0171518
dc.doi.uri (DOI) https://doi.org/10.1371/journal.pone.0171518