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http://dx.doi.org/10.9708/jksci.2014.19.9.125

Response Modeling with Semi-Supervised Support Vector Regression  

Kim, Dong-Il (System Engineering Team, Samsung Electronics, Co. Ltd.)
Publication Information
Journal of the Korea Society of Computer and Information / v.19, no.9, 2014 , pp. 125-139 More about this Journal
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, unlabeled data in the customer dataset are used with the labeled data during training. The proposed SS-SVR algorithm is designed to be a batch learning to reduce the training complexity. The label distributions 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 label distributions with oversampling to construct the training dataset with the labeled data. Finally, a data selection algorithm, Expected Margin based Pattern Selection (EMPS), is employed to reduce the training complexity. The experimental results conducted on a real-world marketing dataset showed that the proposed response modeling method trained efficiently, and improved the accuracy and the expected profit.
Keywords
Response Modeling; Semi-Supervised Learning; Support Vector Regression; Customer Relationship Management; Data Mining;
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Times Cited By KSCI : 2  (Citation Analysis)
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