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An enhanced Credit Card Risk Identification method combining Random Forest Classifier for feature selection and Support Vector Machine classification outperforms Local Outlier Factor, Isolation Forest, and Decision Tree algorithms in detecting fraud risk on large datasets. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

January 1, 2020Procedia ManufacturingOpen Access

Selection Features and Support Vector Machine for Credit Card Risk Identification

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NRNaoufal RtayliAbdelmalek Essaâdi University NENourddine EnneyaUniversité Ibn-Tofail

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Rtayli et al. (2020) studied this question.

synapsesocial.com/papers/6a0f3835f27f69a1d34267ac https://doi.org/10.1016/j.promfg.2020.05.012

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Selection Features and Support Vector Machine for Credit Card Risk Identification

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Selection Features and Support Vector Machine for Credit Card Risk Identification

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