Evolutionary-based feature selection approaches with new criteria for data mining: A case study of credit approval data

Expert Systems with Applications

Volumn 36, Issue 3 PART 2, 2009, Pages 5900-5908

  Wang, Chia Ming ^a   Huang, Yin Fu ^a  

^a NATIONAL YUNLIN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Data Mining; Evolutionary algorithm; Feature selection; Multi objective optimization

Indexed keywords

DATA MINING; EVOLUTIONARY ALGORITHMS; FEATURE EXTRACTION; METADATA; MULTIOBJECTIVE OPTIMIZATION;

CLASSIFICATION TASKS; FEATURE SELECTION METHODS; FEATURE SELECTION PROBLEM; KNOWLEDGE DISCOVERY PROCESS; MIN-MAX NORMALIZATIONS; MULTI-OBJECTIVE OPTIMIZATION PROBLEM; RELATIVE PERFORMANCE; REPLACEMENT SCHEME;

CLASSIFICATION (OF INFORMATION);

EID: 58349092287     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.07.026     Document Type: Article

References (19)

1. Asuncion, A., & Newman, D. J. (2007). UCI machine learning repository. [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.
2. Cantu-Paz E., Newsam S., and Kamath C. Feature selection in scientific applications. KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (2004), ACM Press, New York, NY, USA
3. Cortes C., and Vapnik V. Support-vector networks. Machine Learning 20 3 (1995) 273-297
4. Cristianini N., and Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods (2000), Cambridge University Press
5. Kalyanmoy D., and Kalyanmoy D. Multi-objective optimization using evolutionary algorithms (2001), Wiley, New York, NY, USA
6. Deb K., Agrawal S., Pratab A., and Meyarivan T. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer M., Deb K., Rudolph G., Yao X., Lutton E., Merelo J.J., and Schwefel H.-P. (Eds). Proceedings of the parallel problem solving from nature VI conference (2000), Springer, Paris, France 849-858 Lecture Notes in Computer Science No. 1917
7. Desai V.S., Crook J.N., and Overstreet Jr. G.A. A comparison of neural network and linear scoring models in credit union environment. European Journal of Operational Research (1996) 24-35
8. Drummond, C., & Holte, R. (2003). C4.5, Class imbalance, and cost sensitivity: Why under-sampling beats over-sampling. In Workshop on learning from imbalanced datasets II (within ICML).
9. Hsu, C. W., Chang, C. C., & Lin, C. J., 2003. A practical guide to support vector classification. Technical report. Taipei: Department of Computer Science and Information Engineering, National Taiwan University.
10. Hsu C.-W., and Lin C.-J. A simple decomposition method for support vector machines. Machine Learning 46 (2002) 291-314
11. Huang, C.-M., Lee, Y-J., Lin, D. K. J., & Huang, S.-Y. (2004). Model selection for support vector machines via uniform design. A special issue on Machine Learning and Robust Data Mining of Computational Statistics and Data Analysis.
12. Huang S.H. Dimensionality reduction in automatic knowledge acquisition: A simple greedy search approach. IEEE Transactions on Knowledge and Data Engineering 15 6 (2003) 1364-1373
13. Kira K., and Rendell L.A. The feature selection problem: Traditional methods and a new algorithm. AAAI (1992) 129-134
14. Lin, H.-T., & Li, L., 2005. Analysis of SAGE results with combined learning techniques. In P. Berka, & B. Crémilleux (Eds.), Proceedings of the ECML/PKDD 2005 discovery challenge (pp. 102-113).
15. Ling, C., Huang, J., & Zhang, H., 2003. AUC: A statistically consistent and more discriminating measure than accuracy. In Proceedings of international joint conferences on artificial intelligence.
16. Montgomery D.C., and Runger G.C. Applied statistics and probability for engineers (2006), Wiley
17. Thomas L.C. A survey of credit and behavioral scoring: forecasting financial risk of lending to consumers. International Journal of Forecasting 16 2 (2000) 149-172
18. Weiss, G., & Provost, F. (2001). The effect of class distribution on classifier learning. Technical report ML-TR 43. Department of Computer Science, Rutgers University.
19. Witten I.H., and Frank E. Data mining: Practical machine learning tools and techniques. 2nd ed. (2005), Morgan Kaufman