Affiliations: [a] Department of Information and Communication Systems Engineering, University of the Aegean, Samos, 83200 Greece | [b] Department of Informatics, Technological Education Institute of Athens, Egaleo, 12210 Greece | [c] Department of Electrical and Computer Engineering, National Technical University of Athens, Athens, 15771 Greece
Correspondence:
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Corresponding author: Department of Information and Communication Systems Engineering, University of the Aegean, Karlovassi, Samos, 83200 Greece. Tel.: +30 22730 82234; Fax: +30 22730 82009; E-mail: pbelsis@aegean.gr.
Abstract: E-mail abuse has been steadily increasing during the last decade. E-mail users find themselves targeted by massive quantities of unsolicited bulk e-mail, which often contains offensive language or has fraudulent intentions. Internet Service Providers (ISPs) on the other hand, have to face a considerable system overloading as the incoming mail consumes network and storage resources. Among the plethora of solutions, the most prominent in terms of cost efficiency and complexity are the text filtering approaches. Most of the approaches model the problem using linear statistical models. Despite their popularity – due both to their simplicity and relative ease of interpretation – the non-linearity assumption of data samples is inappropriate in practice. This is mainly due to the inability of other approaches to capture the apparent non-linear relationships, which characterize these samples. In this paper, we propose a margin-based feature selection approach integrated with a Hierarchical Mixtures of Experts (HME) system, which attempts to overcome limitations common to other machine-learning based approaches. By reducing the data dimensionality using effective algorithms for feature selection we evaluated our system with publicly available corpora of e-mails, characterized by very high similarity between legitimate and bulk e-mail (and thus low discriminative potential). We experimented with two different architectures, a hierarchical HME and a perceptron HME. As a result, we confirm the domination of our Spam Filtering (SF) – HME method against other machine learning approaches, which present lesser degree of recall, as well as against traditional rule-based approaches, which lack considerably in the achieved degrees of precision.
Keywords: Spam mail, machine learning based processing, hierarchical mixtures of experts
