Research Area: Machine Learning
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.
Keywords:
Incremental Learning
Concept Drift
Spam Filtering
Machine Learning
Deep Learning
Author(s) Name: Jyh-Jian Sheu,Ko-Tsung Chu ,Nien-Feng Li,Cheng-Chi Lee
Journal name: PLOS ONE
Conferrence name:
Publisher name: PLOS
DOI: 10.1371/journal.pone.0171518
Volume Information: Vol.12, No.2
