A novel multi-stage hybrid model with enhanced multi-population niche genetic algorithm: An application in credit scoring. (1st May 2019)

• Record Type: Journal Article
• Authors: Zhang, Wenyu; He, Hongliang; Zhang, Shuai
• Is Part Of: Expert systems with applications. Volume 121(2019)
• Page Start: 221
• Page End: 232
• Publication Date: 2019-05-01
• Subjects: Machine learning -- Multi-stage hybrid model -- Feature selection -- Classifier selection -- Credit scoring; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.12.020
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A novel multi-stage hybrid model is proposed and applied to credit scoring. Multi-population niche GA (MPNGA) is proposed to improve search efficiency. Feature/classifier selection enables the acquisition of optimal subset. The stacking-based ensemble is constructed to enhance predictive effectiveness. The proposed model is validated on five datasets over four performance metrics. Abstract: In recent years, artificial intelligence and machine learning technology have made great progress and development. Various novel models have been constructed to enhance prediction performance of binary classification from different aspects. Credit scoring model is a typical application of artificial intelligence and machine learning technology. In this study, we propose a novel multi-stage hybrid model, which combines feature selection and classifier selection to obtain optimal feature subset and optimal classifier subset, then uses classifier ensemble to improve the prediction performance based on the two optimal subsets mentioned above. We also extend genetic algorithm, i.e., propose an enhanced multi-population niche genetic algorithm (EMPNGA), to improve the ability of optimization effectively by enhancing the selection, crossover, and mutation steps, and adding niche and migration steps. Furthermore, EMPNGA is applied to combine several filter methods and priori knowledge in feature selection and classifier selection respectively to further increase the search efficiency. The proposed model is applied to credit scoring to verify its prediction performance. Finally, five datasets and four evaluation metrics are applied in the experiment. The experimental results confirm that the performance of proposed model is superior to the other comparative models, proving that this study is of significance and effectiveness.