IE interfaces (산업공학)

Korean Institute of Industrial Engineers (대한산업공학회)
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Feature Selecting and Classifying Integrated Neural Network Algorithm for Multi-variate Classification

다변량 데이터의 분류 성능 향상을 위한 특질 추출 및 분류 기법을 통합한 신경망 알고리즘

  • Yoon, Hyun-Soo (School of Industrial Management Engineering, Korea University) ;
  • Baek, Jun-Geol (School of Industrial Management Engineering, Korea University)
  • 윤현수 (고려대학교 산업경영공학과) ;
  • 백준걸 (고려대학교 산업경영공학과)
  • Received : 2010.12.15
  • Accepted : 2011.01.16
  • Published : 2011.06.01
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Abstract

Research for multi-variate classification has been studied through two kinds of procedures which are feature selection and classification. Feature Selection techniques have been applied to select important features and the other one has improved classification performances through classifier applications. In general, each technique has been independently studied, however consideration of the interaction between both procedures has not been widely explored which leads to a degraded performance. In this paper, through integrating these two procedures, classification performance can be improved. The proposed model takes advantage of KBANN (Knowledge-Based Artificial Neural Network) which uses prior knowledge to learn NN (Neural Network) as training information. Each NN learns characteristics of the Feature Selection and Classification techniques as training sets. The integrated NN can be learned again to modify features appropriately and enhance classification performance. This innovative technique is called ALBNN (Algorithm Learning-Based Neural Network). The experiments' results show improved performance in various classification problems.

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References

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