Combined Classifier for Website Messages Filtration

Abstract

The paper describes a new approach to website messages filtration using combined classifier. Information security standards for the internet resources require user data protection however the increasing volume of spam messages in interactive sections of websites poses a special problem. Unlike many email filtering solutions the proposed approach is based on the effective combination of Bayes and Fisher methods, which allows us to build accurate and stable spam filter. In this paper we consider the organization of combined classifier according to determined optimization criteria based on statistical methods, probability calculations and decision rules.