Jae Hoon Kim , Kang Min Kim

The KIPS Transactions:PartB , Vol. 13, No. 2, pp. 163-170, Apr. 2006
10.3745/KIPSTB.2006.13.2.163,   PDF Download:

Abstract

With rapidly developing Internet applications, an e-mail has been considered as one of the most popular methods for exchanging information. The e-mail, however, has a serious problem that users can receive a lot of unwanted e-mails, what we called, spam mails, which cause big problems economically as well as socially. In order to block and filter out the spam mails, many researchers and companies have performed many sorts of research on spam filtering. In general, users of e-mails have different criteria on deciding if an e-mail is spam or not. Furthermore, in e-mail client systems, users do different actions according to a spam mail or not. In this paper, we propose a mail filtering system using such user actions. The proposed system consists of two steps: One is an action inference step to draw user actions from an e-mail and the other is a mail classification step to decide if the e-mail is spam or not. All the two steps use incremental learning, of which an algorithm is IB2 of TiMBL. To evaluate the proposed system, we collect 12,000 mails of 12 persons. The accuracy is 81 ～ 93% according to each person. The proposed system outperforms, at about 14% on the average, a system that does not use any information about user actions.

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