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Response Modeling with Support Vector Machines




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.

Author(s): Shin, H. and Cho, S.
Journal: Expert Systems with Applications
Volume: 30
Number (issue): 4
Pages: 746-760
Year: 2006
Month: May
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1016/j.eswa.2005.07.037
Institution: Seoul National University, Seoul, Korea
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Response Modeling with Support Vector Machines},
  author = {Shin, H. and Cho, S.},
  journal = {Expert Systems with Applications},
  volume = {30},
  number = {4},
  pages = {746-760},
  organization = {Max-Planck-Gesellschaft},
  institution = {Seoul National University, Seoul, Korea},
  school = {Biologische Kybernetik},
  month = may,
  year = {2006},
  month_numeric = {5}