Ensemble Classifier for Direct Marketing

Data mining is a process of automatically searching large volumes of data for patterns. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. 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. This paper focuses on classification method involving radial basis function and an ensemble of radial basis function. Finally, we propose hybrid architecture involving ensemble and base classifiers for Direct Marketing. The analysis results show that the performance of the proposed method is superior to single usage of existing classification method. We show that the proposed method provides significant improvement of prediction accuracy in direct marketing.