Abstract
Extensive research has been done on feature selection and data classification. But it is not clear which feature selection approach may result in better classification performance on which dataset. So, the comparative performance analysis is required to test the classification performance on the dataset along with feature selection approach. Main aim of this work is to use various feature selection approaches and classifiers for the evaluation of performances of respective classifier along with feature selection approach. Obtained results are compared in terms of accuracy and G-measure. As in many studies, it is shown that ensemble classifier has better performance as compared to individual base classifiers. Further, five heterogeneous classifiers are aggregated with the four ensemble frameworks as majority voting and weighted voting in single and multiple layers as well and results are compared in terms of accuracy, sensitivity, specificity, and G-measure on Australian credit scoring and German loan approval datasets obtained from UCI repository.
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References
Asuncion, A.: D.N.: UCI Machine Learning Repository (2007). http://www.ics.uci.edu/sim/mlearn/MLRepository.html
Ala’raj, M., Abbod, M.F.: Classifiers consensus system approach for credit scoring. Knowl.-Based Syst. 104, 89–105 (2016)
Ala’raj, M., Abbod, M.F.: A new hybrid ensemble credit scoring model based on classifiers consensus system approach. Expert Syst. Appl. 64, 36–55 (2016)
Bashir, S., Qamar, U., Khan, F.H.: Intellihealth: a medical decision support application using a novel weighted multi-layer classifier ensemble framework. J. Biomed. Inf. 59, 185–200 (2016)
Bashir, S., Qamar, U., Khan, F.H., Naseem, L.: Hmv: a medical decision support framework using multi-layer classifiers for disease prediction. J. Comput. Sci. 13, 10–25 (2016)
Mester, L.J., et al.: Whats the point of credit scoring? Bus. Rev. 3(Sep/Oct), 3–16 (1997)
Paleologo, G., Elisseeff, A., Antonini, G.: Subagging for credit scoring models. Eur. J. Oper. Res. 201(2), 490–499 (2010)
Parvin, H., MirnabiBaboli, M., Alinejad-Rokny, H.: Proposing a classifier ensemble framework based on classifier selection and decision tree. Eng. Appl. Artif. Intell. 37, 34–42 (2015)
Ping, Y., Yongheng, L.: Neighborhood rough set and svm based hybrid credit scoring classifier. Expert Syst. Appl. 38(9), 11300–11304 (2011)
Triantaphyllou, E.: Multi-criteria decision making methods. In: Multi-criteria Decision Making Methods: a Comparative Study, pp. 5–21. Springer (2000)
Yao, P.: Hybrid classifier using neighborhood rough set and SVM for credit scoring. In: International Conference on Business Intelligence and Financial Engineering, 2009. BIFE’09, pp. 138–142. IEEE (2009)
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Tripathi, D., Cheruku, R., Bablani, A. (2018). Relative Performance Evaluation of Ensemble Classification with Feature Reduction in Credit Scoring Datasets. In: Reddy Edla, D., Lingras, P., Venkatanareshbabu K. (eds) Advances in Machine Learning and Data Science. Advances in Intelligent Systems and Computing, vol 705. Springer, Singapore. https://doi.org/10.1007/978-981-10-8569-7_30
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DOI: https://doi.org/10.1007/978-981-10-8569-7_30
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