Combined classifiers with neural fuser for spam detection

Abstract

Nowadays combining approach to classification is one of the most promising directions in the pattern recognition. There are many methods of decision making which could be used by the ensemble of classifiers. This work focuses on the fuser design to improve spam detection. We assume that we have a pool of diverse individual classifiers at our disposal and it can grow according the change of spam model. We propose to train a fusion block by the algorithm which has its origin in neural approach and the details and evaluations of mentioned method were presented in the previous works of authors. This work presents the results of computer experiments which were carried out on the basis of exemplary unbalanced spam dataset. They confirm that proposed compound classifier is further step in email security. © 2013 Springer-Verlag Berlin Heidelberg.