Earticle

초록

영어

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.

목차

I. 서론
 II. 문헌 연구
  2.1 사례기반추론
  2.2 GA를 이용한 사례기반추론의 최적화
  2.3 k-NN 결합 유사사례 개수의 최적화
 III. k-NN의 파라미터 k에 대한 GA 최적화
 IV. 실험 설계
  4.1 실험 데이터
  4.2 실험 설계 및 실험용 시스템 개발
 V. 실험 결과
 VI. 결론
 참고문헌

키워드

저자

• 김경재 [ Kyoung-Jae Kim | 동국대학교 경영정보학과 ] 교신저자
• 안현철 [ Hyunchul Ahn | 한국과학기술원 테크노경영대학원 ]
• 한인구 [ Ingoo Han | 한국과학기술원 테크노경영대학원 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  Asia Pacific Journal of Information Systems

• 간기

  계간

• pISSN

  2288-5404

• eISSN

  2288-6818

• 수록기간

  1990~2026

• 등재여부

  KCI 등재,SCOPUS

• 십진분류

  KDC 325 DDC 658