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政大機構典藏 > 傳播學院 > 廣播電視學系 > 期刊論文 > Item 140.119/118158

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/118158

Title:	An efficient incremental learning mechanism for tracking concept drift in spam filtering
Authors:	許志堅 Sheu, Jyh‐Jian Chu, Ko-Tsung Li, Nien-Feng Lee, Cheng-Chi
Contributors:	傳播學院
Date:	2017-02
Issue Date:	2018-06-29 17:12:52 (UTC+8)
Abstract:	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.
Relation:	PLOS ONE 【SCIE】, Vol.12, No.2, pp.e0171518
Data Type:	article
DOI 連結:	https://doi.org/10.1371/journal.pone.0171518
DOI:	10.1371/journal.pone.0171518
Appears in Collections:	[廣播電視學系] 期刊論文

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