An efficient incremental learning mechanism for tracking concept drift in spam filtering.
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.
College of Communication National Chengchi University Taipei, Taiwan, R.O.C.
Jyh-Jian
JJ
Sheu
Department of Finance Minghsin University of Science and Technology Hsinchu, Taiwan, R.O.C.
Ko-Tsung
KT
Chu
Yilan County Government Police Bureau Yilan, Taiwan, R.O.C.
Nien-Feng
NF
Li
Department of Library and Information Science Fu Jen Catholic University New Taipei, Taiwan, R.O.C.|Department of Photonics and Communication Engineering Asia University Taichung, Taiwan, R.O.C.
Cheng-Chi
CC
Lee
D034742:Electronic Mail / Q000592:standards*; D016247:Information Storage and Retrieval / Q000379:methods; D020407:Internet / Q000592:standards*; D000069550:Machine Learning