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  Response Modeling with Support Vector Machines

Shin, H., & Cho, S. (2006). Response Modeling with Support Vector Machines. Expert Systems with Applications, 30(4), 746-760. doi:10.1016/j.eswa.2005.07.037.

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Shin, H1, Author              
Cho, S, Author
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1Friedrich Miescher Laboratory, Max Planck Society, Max-Planck-Ring 9, 72076 Tübingen, DE, ou_2575692              

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 Abstract: Support Vector Machine (SVM) employs Structural Risk minimization (SRM) principle to generalize better than conventional machine learning methods employing the traditional Empirical Risk Minimization (ERM) principle. When applying SVM to response modeling in direct marketing,h owever,one has to deal with the practical difficulties: large training data,class imbalance and binary SVM output. This paper proposes ways to alleviate or solve the addressed difficulties through informative sampling,u se of different costs for different classes, and use of distance to decision boundary. This paper also provides various evaluation measures for response models in terms of accuracies,lift chart analysis and computational efficiency.

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 Dates: 2006-05
 Publication Status: Published in print
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 Identifiers: DOI: 10.1016/j.eswa.2005.07.037
BibTex Citekey: 2715
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Title: Expert Systems with Applications
Source Genre: Journal
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Pages: - Volume / Issue: 30 (4) Sequence Number: - Start / End Page: 746 - 760 Identifier: -