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Response Modeling with Semi-Supervised Support Vector Regression

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2014, 19(9), pp.125-139
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

Dongil Kim 1

1삼성전자

Accredited

ABSTRACT

In this paper, I propose a response modeling with a Semi-Supervised Support Vector Regression(SS-SVR) algorithm. In order to increase the accuracy and profit of response modeling, unlabeleddata in the customer dataset are used with the labeled data during training. The proposed SS-SVRalgorithm is designed to be a batch learning to reduce the training complexity. The labeldistributions of unlabeled data are estimated in order to consider the uncertainty of labeling. Then, multiple training data are generated from the unlabeled data and their estimated labeldistributions with oversampling to construct the training dataset with the labeled data. Finally, adata selection algorithm, Expected Margin based Pattern Selection (EMPS), is employed to reducethe training complexity. The experimental results conducted on a real-world marketing datasetshowed that the proposed response modeling method trained efficiently, and improved theaccuracy and the expected profit.

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