Journal of the Korean Operations Research and Management Science Society (한국경영과학회지)

The Korean Operations Research and Management Science Society (한국경영과학회)
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A Hybrid Data Mining Technique Using Error Pattern Modeling

오차 패턴 모델링을 이용한 Hybrid 데이터 마이닝 기법

  • 허준 (SPSS Korea (주)데이터솔루션) ;
  • 김종우 (한양대학교 경영대학 경영학부)
  • Published : 2005.12.01
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Abstract

This paper presents a new hybrid data mining technique using error pattern modeling to improve classification accuracy when the data type of a target variable is binary. The proposed method increases prediction accuracy by combining two different supervised learning methods. That is, the algorithm extracts a subset of training cases that are predicted inconsistently by both methods, and models error patterns from the cases. Based on the error pattern model, the Predictions of two different methods are merged to generate final prediction. The proposed method has been tested using practical 10 data sets. The analysis results show that the performance of proposed method is superior to the existing methods such as artificial neural networks and decision tree induction.

Keywords

References

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