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The Korea Institute of Information and Commucation Engineering 2005; 3(4): 209-212

Published online December 1, 2005

© Korea Institute of Information and Communication Engineering

Hybrid Case-based Reasoning and Genetic Algorithms Approach for Customer Classification

Kim Kyoung-jae;Ahn Hyunchul;

Department of Information Systems, Dongguk University, Graduate School of Management, KAIST

Abstract

This study proposes hybrid case-based reasoning and genetic algorithms model for customer classification. In this study, vertical and horizontal dimensions of the research data are reduced through integrated feature and instance selection process using genetic algorithms. We applied the proposed model to customer classification model which utilizes customers' demographic characteristics as inputs to predict their buying behavior for the specific product. Experimental results show that the proposed model may improve the classification accuracy and outperform various optimization models of typical CBR system.

Keywords Case-based reasoning,genetic algorithms,feature selection,instance selection,customer classification.

Article

The Korea Institute of Information and Commucation Engineering 2005; 3(4): 209-212

Published online December 1, 2005

Copyright © Korea Institute of Information and Communication Engineering.

Hybrid Case-based Reasoning and Genetic Algorithms Approach for Customer Classification

Kim Kyoung-jae;Ahn Hyunchul;

Department of Information Systems, Dongguk University, Graduate School of Management, KAIST

Abstract

This study proposes hybrid case-based reasoning and genetic algorithms model for customer classification. In this study, vertical and horizontal dimensions of the research data are reduced through integrated feature and instance selection process using genetic algorithms. We applied the proposed model to customer classification model which utilizes customers' demographic characteristics as inputs to predict their buying behavior for the specific product. Experimental results show that the proposed model may improve the classification accuracy and outperform various optimization models of typical CBR system.

Keywords: Case-based reasoning,genetic algorithms,feature selection,instance selection,customer classification.

JICCE
Jun 30, 2024 Vol.22 No.2, pp. 109~97

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Journal of Information and Communication Convergence Engineering Jouranl of information and
communication convergence engineering
(J. Inf. Commun. Converg. Eng.)

eISSN 2234-8883
pISSN 2234-8255