Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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Boosted Regression Method based on Rejection Limits for Large-Scale Data

대량 데이터를 위한 제한거절 기반의 회귀부스팅 기법

  • Kwon, Hyuk-Ho (Dept. of Mechanical convergence Engineering, Hanyang University) ;
  • Kim, Seung-Wook (Dept. of Mechanical convergence Engineering, Hanyang University) ;
  • Choi, Dong-Hoon (School of Mechanical Engineering, Hanyang University) ;
  • Lee, Kichun (Industrial Engineering, Hanyang University)
  • 권혁호 (한양대학교 융합기계공학과) ;
  • 김승욱 (한양대학교 융합기계공학과) ;
  • 최동훈 (한양대학교 기계공학과) ;
  • 이기천 (한양대학교 산업공학과)
  • Received : 2016.02.17
  • Accepted : 2016.06.09
  • Published : 2016.08.15
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Abstract

The purpose of this study is to challenge a computational regression-type problem, that is handling large-size data, in which conventional metamodeling techniques often fail in a practical sense. To solve such problems, regression-type boosting, one of ensemble model techniques, together with bootstrapping-based re-sampling is a reasonable choice. This study suggests weight updates by the amount of the residual itself and a new error decision criterion which constructs an ensemble model of models selectively chosen by rejection limits. Through these ideas, we propose AdaBoost.RMU.R as a metamodeling technique suitable for handling large-size data. To assess the performance of the proposed method in comparison to some existing methods, we used 6 mathematical problems. For each problem, we computed the average and the standard deviation of residuals between real response values and predicted response values. Results revealed that the average and the standard deviation of AdaBoost.RMU.R were improved than those of other algorithms.

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