Cetiner, ErkanGüngör, Vehbi ÇağrıKocak, Taskin2024-06-052024-06-052018201897898196989369789819698042978981969811097898196989059789819512324978303202601997830320089099783031915802978981969814197830319841361611-33490302-9743https://doi.org/10.1007/978-3-319-96133-0_6https://hdl.handle.net/20.500.12573/2185Hybrid classification approaches on credit domain are widely used to obtain valuable information about customer behaviours. Single classification algorithms such as neural networks, support vector machines and regression analysis have been used since years on related area. In this paper, we propose hybrid classification approaches, which try to combine several classifiers and ensemble learners to boost accuracy on classification results. We worked with two credit datasets, German dataset which is a public dataset and a Turkish Corporate Bank dataset. The goal of using such diverse datasets is to search for generalization ability of proposed model. Results show that feature selection plays a vital role on classification accuracy, hybrid approaches which shaped with ensemble learners outperform single classification techniques and hybrid approaches which consists SVM has better accuracy performance than other hybrid approaches. © 2018 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/closedAccessCredit-RiskFeature SelectionHybrid-ClassifierArtificial IntelligenceData MiningFeature ExtractionRegression AnalysisRisk AssessmentSupport Vector MachinesClassification AccuracyClassification AlgorithmClassification ResultsClassification TechniqueCredit RisksGeneralization AbilityHybrid ClassificationHybrid ClassifierClassification (Of Information)Conference Object10.1007/978-3-319-96133-0_62-s2.0-85050475585