We introduce a semi-supervised active learning system for fraud detection; we select a semi-supervised approach due to the fact that typical available datasets are totally unlabeled or only partially labeled, moreover the active learning methodology allows us to obtain very good results by requiring a lower number of labels with respect to traditional approaches.
A Semi-supervised Active Learning System for Fraud Detection
DI BLASI, Gianpiero;MEO, Rosa;PENSA, Ruggero Gaetano
2013-01-01
Abstract
We introduce a semi-supervised active learning system for fraud detection; we select a semi-supervised approach due to the fact that typical available datasets are totally unlabeled or only partially labeled, moreover the active learning methodology allows us to obtain very good results by requiring a lower number of labels with respect to traditional approaches.
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