Incremental learning framework for real‐world fraud detection environment. (28th January 2021)

• Record Type: Journal Article
• Authors: Anowar, Farzana; Sadaoui, Samira
• Is Part Of: Computational intelligence. Volume 37:Number 1(2021)
• Page Start: 635
• Page End: 656
• Publication Date: 2021-01-28
• Subjects: chunk‐based incremental learning -- fraud detection -- imbalanced data -- incremental memory model -- incremental SGD -- one‐class SVM; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12434
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: For detecting malicious bidding activities in e‐auctions, this study develops a chunk‐based incremental learning framework that can operate in real‐world auction settings. The self‐adaptive framework first classifies incoming bidder chunks to counter fraud in each auction and take necessary actions. The fraud classifier is then adjusted with confident bidders' labels validated via bidder verification and one‐class classification. Based on real fraud data produced from commercial auctions, we conduct an extensive experimental study wherein the classifier is adapted incrementally using only relevant bidding data while evaluating the subsequent adjusted models' detection and misclassification rates. We also compare our classifier with static learning and learning without data relevancy.