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A Hybrid Data Mining Technique Using Error Pattern Modeling  

Hur, Joon (SPSS Korea (주)데이터솔루션)
Kim, Jong-Woo (한양대학교 경영대학 경영학부)
Publication Information
Journal of the Korean Operations Research and Management Science Society / v.30, no.4, 2005 , pp. 27-43 More about this Journal
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
Supervised learning; Hybrid Model; Combined Model; Voting; Error Pattern Modeling;
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