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

TL;DR

This paper introduces an incremental personalized email spam filter that uses a novel feature selection method called TFDCR and dynamic feature updates to adapt to changing spam content, improving accuracy and reducing false positives.

Contribution

It proposes a new feature selection method TFDCR, an incremental learning model with dynamic feature updates, and a heuristic for selecting discriminative features from incoming emails.

Findings

TFDCR outperforms existing feature selection methods.

The incremental model adapts to evolving spam content effectively.

The filter reduces false positives and improves classification accuracy.

Abstract

Communication through e-mails remains to be highly formalized, conventional and indispensable method for the exchange of information over the Internet. An ever-increasing ratio and adversary nature of spam e-mails have posed a great many challenges such as uneven class distribution, unequal error cost, frequent change of content and personalized context-sensitive discrimination. In this research, we propose a novel and distinctive approach to develop an incremental personalized e-mail spam filter. The proposed work is described using three significant contributions. First, we applied a novel term frequency difference and category ratio based feature selection function TFDCR to select the most discriminating features irrespective of the number of samples in each class. Second, an incremental learning model is used which enables the classifier to update the discriminant function dynamically. Third, a heuristic function called selectionRankWeight is introduced to upgrade the existing feature set that determines new features carrying strong discriminating ability from an incoming set of e-mails. Three public e-mail datasets possessing different characteristics are used to evaluate the filter performance. Experiments are conducted to compare the feature selection efficiency of TFDCR and to observe the filter performance under both the batch and the incremental learning mode. The results demonstrate the superiority of TFDCR as the most effective f eature selection function. The incremental learning model incorporating dynamic feature update function overcomes the problem of drifting concepts. The proposed filter validates its efficiency and feasibility by substantially improving the classification accuracy and reducing the false positive error of misclassifying legitimate e-mail as spam.