Spam E-mail Classification Based on the IFWB Algorithm

doi:10.1007/978-3-642-36546-1_33

機構典藏 > College of Business and Management > Graduate Institute & Department of Information Management > Journal Article > Item 987654321/108550

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/108550

Title:	Spam E-mail Classification Based on the IFWB Algorithm
Authors:	Jou, Chi-Chang
Keywords:	spam classification;incremental forgetting;misclassification cost
Date:	2013-03-19
Issue Date:	2016-11-26 02:11:47 (UTC+8)
Publisher:	Springer Berlin Heidelberg
Abstract:	The problem of spam overflow has not been solved completely. Many anti-spam techniques have been proposed. Among them, the machine learning techniques are the most popular, but these works are based on a static environment assumption. In the real world application, the email context may change with concept drift. The classification result is usually good at the beginning, but along with time evolution and concept drift, the classification accuracy dropped down gradually. So a mechanism is needed to adjust the classifier according to the new incoming emails and the old emails in the dataset. Another problem of email categorization is data skewedness. Because of the spam overflow, the number of spam emails is far more than that of legitimate ones. In the classification result, the majority class is with higher recall rate, but the minority class with poor recall rate. For these reasons, we propose an algorithm, IFWB (Incremental Forgetting Weighted Bayesian), based on Naïve Bayesian and IGICF (Information Gain and Inverse Class Frequency) feature extraction, combined with gradual forgetting mechanism and cost-sensitive model to tackle concept drift and data skewedness. Finally, we demonstrate the effectiveness of the IFWB algorithm through a series of experiments.
Relation:	Lecture Notes in Artificial Intelligence 7802, pp.314-324
DOI:	10.1007/978-3-642-36546-1_33
Appears in Collections:	[Graduate Institute & Department of Information Management] Journal Article

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	166	View/Open
Spam E-mail Classification Based on the IFWB Algorithm(1).pdf		502Kb	Adobe PDF	1	View/Open

Loading...