Global optimization of feature weights and the number of neighbors that combine in a case-based reasoning system

Expert Systems

Volumn 23, Issue 5, 2006, Pages 290-301

  Ahn, Hyunchul ^a   Kim, Kyoung Jae ^b   Man, Ingoo ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

^b Dongguk University   (South Korea)

Author keywords

Case based reasoning; Feature weighting; Genetic algorithm; Number of neighbors that combine

Indexed keywords

CASE BASED REASONING (CBR); CLASSIFICATION PROBLEMS; FEATURE WEIGHTING;

ARTIFICIAL INTELLIGENCE; FORMAL LOGIC; GENETIC ALGORITHMS; GLOBAL OPTIMIZATION; INFORMATION RETRIEVAL; MANUFACTURE; MATHEMATICAL MODELS; OPTIMIZATION;

FEATURE EXTRACTION;

EID: 33749996478     PISSN: 02664720     EISSN: 14680394     Source Type: Journal    
DOI: 10.1111/j.1468-0394.2006.00410.x     Document Type: Conference Paper

References (22)

1. BRADLEY, P. (1994) Case-based reasoning: business applications, Communications of the ACM, 37 (3), 40-43.
2. CHIU, C. (2002) A case-based customer classification approach for direct marketing, Expert Systems with Applications, 22, 163-168.
3. CHIU, C., P.C. CHANG and N.H. CHIU (2003) A casebased expert support system for due-date assignment in a water fabrication factory, Journal of Intelligent Manufacturing, 14, 287-296.
4. FU, Y. and R. SHEN (2004) GA based CBR approach in Q&A system, Expert Systems with Applications, 26 (2), 167-170.
5. GARRELL I GUIU, J.M., E. GOLOBARDES I RIBÉ, E. BERNADO I MANSILLA and X. LLORÀI FÀBREGA (1999) Automatic diagnosis with genetic algorithms and case-based reasoning, Artificial Intelligence in Engineering, 13, 367-372.
6. HAN, J. and M. KAMBER (2001) Datamining: Concepts and Techniques, San Francisco, CA: Morgan Kaufmann.
7. HARNETT, D.L. and A.K. SONI (1991) Statistical Methods for Business and Economics, Reading, MA: Addison-Wesley.
8. HEO, J. (2001) Data Mining in Telecommunications Market: Telco-CAT, Seoul: SPSS Korea.
9. JARMULAK, J., S. CRAW and R. ROWE (2000). Selfoptimizing CBR retrieval, Proceedings of the Twelfth IEEE International Conference on Tools with Artificial Intelligence, New York: IEEE, 376-383.
10. KELLY, J.D.J. and L. DAVIS (1991) Hybridizing the genetic algorithm and the k nearest neighbors classification algorithm, Proceedings of the Fourth International Conference on Genetic Algorithms, San Diego, CA: Morgan Kaufmann, 377-383.
11. KIM, K. (2004) Toward global optimization of casebased reasoning systems for financial forecasting, Applied Intelligence, 21 (3), 239-249.
12. KIM, K. and I. HAN (2001) Maintaining case-based reasoning systems using a genetic algorithms approach, Expert Systems with Applications, 21, 139-145.
13. KOLODNER, J. (1993) Case-based Reasoning, San Mateo, CA: Morgan Kaufmann.
14. KUNCHEVA, L.I. and L.C. JAIN (1999) Nearest neighbor classifier; simultaneous editing and feature selection, Pattern Recognition Letters, 20, 1149-1156.
15. LEE, H.Y. and K.N. PARK (1999) Methods for determining the optimal number of cases that combine in an effective case based forecasting system, Korean Journal of Management Research, 27, 1239-1252.
16. LIAO, T.W., Z.M. ZHANG and C.R. MOUNT (2000) A case-based reasoning system for identifying failure mechanisms, Engineering Applications of Artificial Intelligence, 13, 199-213.
17. ROZSYPAL, A. and M. KUBAT (2003) Selecting representative examples and attributes by a genetic algorithm, Intelligent Data Analysis, 7, 291-304.
18. SHIN, K.S. and I. HAN (1999) Case-based reasoning supported by genetic algorithms for corporate bond rating, Expert Systems with Applications, 16, 85-95.
19. SHIN, T. and I. HAN (2000) Optimal signal multiresolution by genetic algorithms to support artificial neural networks for exchange-rate forecasting, Expert Systems with Applications, 18 (4), 257-269.
20. WANG, Y. and N. ISHII (1997) A method of similarity metrics for structured representations, Expert Systems with Applications, 12, 89-100.
21. WETTSCHERECK, D., D.W. AHA and T. MOHRI (1997) A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms, Artificial Intelligence Review, 11, 273-314.
22. YIN, W.J., M. LIU and C. WU (2002) A genetic learning approach with case-based memory for job-shop scheduling problems, Proceedings of the First International Conference on Machine Learning and Cybernetics, Beijing, IEEE Press, 1683-1687.