Book
Lecture Notes in Networks and Systems (Proceedings of International Conference on Fourth Industrial Revolution and Beyond 2021)
Volume 437
(2022),
p. 357
An Empirical Feature Selection Approach for Phishing Websites Prediction with Machine Learning
Pankaj Bhowmik, Md. Sohrawordi, U. A. Md. Ehsan Ali, Pulak Chandra Bhowmik
Pub. online:17 Jun 2024Type:Research ArticleOpen Access
Journal:Informatica
Volume 35, Issue 3 (2024), pp. 483–507
Abstract
Spam reviews are a pervasive problem on online platforms due to its significant impact on reputation. However, research into spam detection in data streams is scarce. Another concern lies in their need for transparency. Consequently, this paper addresses those problems by proposing an online solution for identifying and explaining spam reviews, incorporating data drift adaptation. It integrates (i) incremental profiling, (ii) data drift detection & adaptation, and (iii) identification of spam reviews employing Machine Learning. The explainable mechanism displays a visual and textual prediction explanation in a dashboard. The best results obtained reached up to 87% spam F-measure.