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4 Problem New Case RETRIEVE Learned Case Retrieved Cases New Case RETAIN Tested/ Repaired Case Case-Base REVISE Solved Case REUSE

14 Aamodt, A. and Plaza, E. (1994). Case-based reasoning; Foundational issues, methodological variations, and system approaches, AI Communications, Vol. 7(1), Ahn, H., Kim, K.-j. and Han, I. (2006a). Hybrid Genetic Algorithms and Case-based Reasoning Systems for Customer Classification, Expert Systems, Vol. 23(3), Ahn, H., Kim, K.-j. and Han, I. (2006b). Global optimization of feature weights and the number of neighbors that combine in a CBR system, Expert Systems, Vol. 23(5), Ahn, H., Kim, K.-j. and Han, I. (2007). A Case-based Reasoning System with the Two- Dimensional Reduction Technique for Customer Classification, Expert Systems with Applications, Vol. 32(4), Chiu, C. (2002). A case-based customer classification approach for direct marketing, Expert Systems with Applications, Vol. 22, Chiu, C., Chang, P. C., and Chiu, N. H. (2003). A case-based expert support system for duedate assignment in a water fabrication factory, Journal of Intelligent Manufacturing, Vol. 14, Garrell i Guiu, J. M., Golobardes i Ribé, E., Bernadó i Mansilla, E., and Llorà i Fàbrega, X. (1999). Automatic diagnosis with genetic algorithms and case-based reasoning, Artificial Intelligence in Engineering, Vol. 13, Kim, K. and Han, I. (2001). Maintaining case-based reasoning systems using a genetic algorithms approach, Expert Systems with Applications, Vol. 21, Kim, T.S., Yoon, J.H., and Lee, H.K. (2002). Performance of a nonparametric multivariate nearest neighbor model in the prediction of stock index returns, Asia Pacific Management Review, Vol. 7, Kumar, V. and Reinartz, W. J. (2006). Customer Relationship Management: A Databased Approach. NJ: John Wiley & Sons. Kuncheva, L. I. and Jain, L. C. (1999). Nearest neighbor classifier: Simultaneous editing and feature selection, Pattern Recognition Letters, Vol. 20,

15 Shin, K. S. and Han, I. (1999). Case-based reasoning supported by genetic algorithms for corporate bond rating, Expert Systems with Applications, Vol. 16, Siedlecki, W. and Sklanski, J. (1989). A note on genetic algorithms for large-scale feature selection, Pattern Recognition Letters, Vol. 10, Sun, J. and Hui, X.-F. (2006). Financial Distress Prediction Based on Similarity Weighted Voting CBR, Lecture Notes in Artificial Intelligence, Vol. 4093,

16 Case-based reasoning(cbr) is a problem solving technique that is quite simple to implement in general, but often handles complex and unstructured decision making problems very effectively. Thus, it has been applied to various problem-solving areas including manufacturing, finance and marketing. Nonetheless, it is never easy to design effective CBR systems because they have several factors for design including issues on 'combining similar cases'. This study proposes a novel CBR model, which explores similar cases in an innovative way. Conventional CBR models determine similar cases according to fixed number of neighbors to combine, or relative similarity ratios. However, our model selects similar cases based on similarity threshold - an absolute value ranging from 0 to 1 - and coverage. To validate the usefulness of our model, we applied it to a case for target marketing of an Internet shopping mall in Korea. As a result, we found that our model might be applied to find appropriate prospects for target marketing in an effective way. *Full-time Instructor, Dept. of Business Administration, SungShin Women's University

김경재 안현철 지능정보연구제 17 권제 4 호 2011 년 12 월

김경재 안현철 지능정보연구제 17 권제 4 호 2011 년 12 월 지능정보연구제 17 권제 4 호 2011 년 12 월 (pp.241~254) Support vector machines(svm),, CRM. SVM,,., SVM,,.,,. SVM, SVM. SVM.. * 2009() (NRF-2009-327- B00212). 지능정보연구제 17 권제 4 호 2011 년 12 월 김경재 안현철 지능정보연구제 17 권제 4 호