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Communication through e-mails remains to be highly formalized, conventionaland indispensable method for the exchange of information over the Internet. Anever-increasing ratio and adversary nature of spam e-mails have posed a greatmany challenges such as uneven class distribution, unequal error cost, frequentchange of content and personalized context-sensitive discrimination. In thisresearch, we propose a novel and distinctive approach to develop an incrementalpersonalized e-mail spam filter. The proposed work is described using threesignificant contributions. First, we applied a novel term frequency differenceand category ratio based feature selection function TFDCR to select the mostdiscriminating features irrespective of the number of samples in each class.Second, an incremental learning model is used which enables the classifier toupdate the discriminant function dynamically. Third, a heuristic functioncalled selectionRankWeight is introduced to upgrade the existing feature setthat determines new features carrying strong discriminating ability from anincoming set of e-mails. Three public e-mail datasets possessing differentcharacteristics are used to evaluate the filter performance. Experiments areconducted to compare the feature selection efficiency of TFDCR and to observethe filter performance under both the batch and the incremental learning mode.The results demonstrate the superiority of TFDCR as the most effective f eatureselection function. The incremental learning model incorporating dynamicfeature update function overcomes the problem of drifting concepts. Theproposed filter validates its efficiency and feasibility by substantiallyimproving the classification accuracy and reducing the false positive error ofmisclassifying legitimate e-mail as spam.

Incremental personalized E-mail spam filter using novel TFDCR feature selection with dynamic feature update