Journal of Information Technology Applications and Management

Korea Data Strategy Society (한국데이터전략학회)
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Genetic Algorithm based Hybrid Ensemble Model

유전자 알고리즘 기반 통합 앙상블 모형

  • Min, Sung-Hwan (Department of Business Administration, Hallym University)
  • Received : 2016.01.21
  • Accepted : 2016.03.22
  • Published : 2016.03.31
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Abstract

An ensemble classifier is a method that combines output of multiple classifiers. It has been widely accepted that ensemble classifiers can improve the prediction accuracy. Recently, ensemble techniques have been successfully applied to the bankruptcy prediction. Bagging and random subspace are the most popular ensemble techniques. Bagging and random subspace have proved to be very effective in improving the generalization ability respectively. However, there are few studies which have focused on the integration of bagging and random subspace. In this study, we proposed a new hybrid ensemble model to integrate bagging and random subspace method using genetic algorithm for improving the performance of the model. The proposed model is applied to the bankruptcy prediction for Korean companies and compared with other models in this study. The experimental results showed that the proposed model performs better than the other models such as the single classifier, the original ensemble model and the simple hybrid model.

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References

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