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APPLICATION OF ADAPTIVE SPLITTING AND SELECTION CLASSIFIER TO THE SPAM FILTERING PROBLEM

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E-Mail spam is one of the major problems plaguing the contemporary Internet, causing an inconvenience to an individual user and financial loss to a company. Spam filtering allows for early detection of unwanted messages and separates them from the incoming e-mail. Nonetheless, designing an effective spam detection system is not a trivial task, due to the problems connected with the analysis of the e-mail content and the occurrence of variation in spam characteristics. This article presents an application of a novel ensemble classifier system for spam detection. The system is an extension of the adaptive splitting and selection (AdaSS) framework. The idea of the ensemble is based on the assumption that high effectiveness of detection can be obtained by exploitation of the local competency of a set of diverse elementary classifiers. Therefore, the ensemble training algorithm divides the feature space into several disjoint subspaces and assigns an area classifier to each of them. The area classifier consists of elementary classifiers that make a collective decision based on the weighted fusion of their support functions. The weight reflects the local competency of the classifier. To maintain the diversity of the pool of elementary classifiers, we exploit different e-mail feature extraction methods while filling the pool. There are two main extensions of the presented algorithm over original AdaSS: the aforementioned weighted fusion model used for decision making and adaptation of the AdaSS training procedure to process data streams featuring the concept drift. The effectiveness of the classifier model in spam recognition was verified in a series of experiments on two sets of spam databases. Comparison of the algorithm with some other state-of-the-art ensemble methods showed that the presented AdaSS extension can effectively recognize local competences of elementary classifiers and result in very high effectiveness of spam recognition outperforming competing methods.
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Keywords: clustering and selection; evolutionary algorithm; machine learning; multiple classifier systems; spam detection; trained fuser

Document Type: Research Article

Affiliations: Department of Systems and Computer Networks, Wrocław University of Technology, Wrocław, Poland

Publication date: October 3, 2013

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