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This study evaluates the efficiency of the hybrid classifier. The study found that the clustering based LR + MLP hybrid classifier efficient across all of the criteria. Among the other classifiers, MLP based hybrid classifiers also achieved superior performance. Abstract This paper examines which hybridization strategy is more suitable for credit risk assessment in the dynamic financial world. As such, we use extensive new data sets and develop different hybrid models by combining traditional statistical and modern artificial intelligence methods based on classification and clustering feature selection approaches. We find that a multilayer perceptron (MLP) combined with discriminant analysis or logistic regression (LR) can significantly improve classification accuracy compared with other single and hybrid classifiers. In particular, the findings of our empirical analysis, statistical significance test and expected cost of misclassification test confirm the superiority of the clus

Statistical analysisArtificial intelligenceHybrid productsCredit riskRisk model validationOriginal researchபுள்ளிவிவர பகுப்பாய்வுசெயற்கை உளவுத்துறைகலப்பு ப்ராடக்ட்ஸ்கடன் ஆபத்துஆபத்து மாதிரி சரிபார்த்தல்ஒரிஜிநல் ஆராய்ச்சி