A Hybrid Framework using RBF and SVM for Direct Marketing

one of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. This paper addresses using an ensemble of classification methods for direct marketing. Direct marketing has become an important application field for data mining. In direct marketing, companies or organizations try to establish and maintain a direct relationship with their customers in order to target them individually for specific product offers or for fund raising. A variety of techniques have been employed for analysis ranging from traditional statistical methods to data mining approaches. In this research work, new hybrid classification method is proposed by combining classifiers in a heterogeneous environment using arcing classifier and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. Here, modified training sets are formed by resampling from original training set; classifiers constructed using these training sets and then combined by voting. Empirical results illustrate that the proposed hybrid systems provide more accurate direct marketing system.

@article{Govidarajan2013,
  title     = {A Hybrid Framework using RBF and SVM for Direct Marketing},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {4},
  number    = {4},
  year      = {2013},
  publisher = {The Science and Information Organization},
  author    = {M. Govidarajan},
  doi       = {10.14569/IJACSA.2013.040419},
  url       = {https://doi.org/10.14569/IJACSA.2013.040419}
}