An Architecture of Active Learning SVMs with Relevance Feedback for Classifying E-mail

TL;DR

This paper introduces an active learning SVM architecture with relevance feedback for email classification, enhancing accuracy and robustness against spam while preventing legitimate emails from being misclassified.

Contribution

It presents a novel architecture combining active learning and relevance feedback for improved email classification with dynamic updates.

Findings

Reduces misclassification of legitimate emails.

Enhances spam detection robustness.

Supports dynamic updating of support vectors.

Abstract

In this paper, we have proposed an architecture of active learning SVMs with relevance feedback (RF)for classifying e-mail. This architecture combines both active learning strategies where instead of using a randomly selected training set, the learner has access to a pool of unlabeled instances and can request the labels of some number of them and relevance feedback where if any mail misclassified then the next set of support vectors will be different from the present set otherwise the next set will not change. Our proposed architecture will ensure that a legitimate e-mail will not be dropped in the event of overflowing mailbox. The proposed architecture also exhibits dynamic updating characteristics making life as difficult for the spammer as possible.