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Relative Performance Evaluation of Ensemble Classification with Feature Reduction in Credit Scoring Datasets

  • Diwakar Tripathi
  • Ramalingaswamy Cheruku
  • Annushree Bablani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 705)

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.

Keywords

Classification Credit scoring Ensemble framework Feature selection 

References

  1. 1.
    Asuncion, A.: D.N.: UCI Machine Learning Repository (2007). http://www.ics.uci.edu/sim/mlearn/MLRepository.html
  2. 2.
    Ala’raj, M., Abbod, M.F.: Classifiers consensus system approach for credit scoring. Knowl.-Based Syst. 104, 89–105 (2016)CrossRefGoogle Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    Mester, L.J., et al.: Whats the point of credit scoring? Bus. Rev. 3(Sep/Oct), 3–16 (1997)Google Scholar
  7. 7.
    Paleologo, G., Elisseeff, A., Antonini, G.: Subagging for credit scoring models. Eur. J. Oper. Res. 201(2), 490–499 (2010)CrossRefGoogle Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    Ping, Y., Yongheng, L.: Neighborhood rough set and svm based hybrid credit scoring classifier. Expert Syst. Appl. 38(9), 11300–11304 (2011)CrossRefGoogle Scholar
  10. 10.
    Triantaphyllou, E.: Multi-criteria decision making methods. In: Multi-criteria Decision Making Methods: a Comparative Study, pp. 5–21. Springer (2000)Google Scholar
  11. 11.
    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)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Diwakar Tripathi
    • 1
  • Ramalingaswamy Cheruku
    • 1
  • Annushree Bablani
    • 1
  1. 1.Department of Computer Sciene and EngineeringNational Institute of Technology GoaPondaIndia

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