Prediction of KOSPI using Data Editing Techniques and Case-based Reasoning (자료편집기법과 사례기반추론을 이용한 한국종합주가지수 예측)

Kyoung-jae Kim (김경재)

Prediction of KOSPI using Data Editing Techniques and Case-based Reasoning

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2007, 12(6), pp.287-296
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science

Kyoung-jae Kim ¹

¹동국대학교

Accredited

ABSTRACT

This paper proposes a novel data editing techniques with genetic algorithm (GA) in case-based reasoning (CBR) for the prediction of Korea Stock Price Index (KOSPI). CBR has been widely used in various areas because of its convenience and strength in complex problem solving. Nonetheless, compared to other machine learning techniques, CBR has been criticized because of its low prediction accuracy. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. In this paper, the GA optimizes simultaneously feature weights and a selection task for relevant instances for achieving good matching and retrieval in a CBR system. This study applies the proposed model to stock market analysis. Experimental results show that the GA approach is a promising method for data editing in CBR.

KEYWORDS

Data editing, Instance selection, Genetic algorithms, Case-based reasoning, Stock market prediction

Citation status

* References for papers published after 2023 are currently being built.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65