Asia pacific journal of information systems

The Korea Society of Management Information Systems (한국경영정보학회)
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Optimization of Case-based Reasoning Systems using Genetic Algorithms: Application to Korean Stock Market

유전자 알고리즘을 이용한 사례기반추론 시스템의 최적화: 주식시장에의 응용

  • 김경재 (동국대학교 경영정보학과) ;
  • 안현철 (한국과학기술원 테크노경영대학원) ;
  • 한인구 (한국과학기술원 테크노경영대학원)
  • Published : 2006.03.31
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Abstract

Case-based reasoning (CBR) is a reasoning technique that reuses past cases to find a solution to the new problem. It often shows significant promise for improving effectiveness of complex and unstructured decision making. It has been applied to various problem-solving areas including manufacturing, finance and marketing for the reason. However, the design of appropriate case indexing and retrieval mechanisms to improve the performance of CBR is still a challenging issue. Most of the previous studies on CBR have focused on the similarity function or optimization of case features and their weights. According to some of the prior research, however, finding the optimal k parameter for the k-nearest neighbor (k-NN) is also crucial for improving the performance of the CBR system. In spite of the fact, there have been few attempts to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. In this study, we introduce a genetic algorithm (GA) to optimize the number of neighbors to combine. This study applies the novel approach to Korean stock market. Experimental results show that the GA-optimized k-NN approach outperforms other AI techniques for stock market prediction.

Keywords

References

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