Performance Evaluation of Bagged RBF Classifier for Data Mining Applications

Data mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in databases process. 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. The feasibility and the benefits of the proposed approaches are demonstrated by the means of data mining applications like intrusion detection, direct marketing, and signature verification. A variety of techniques have been employed for analysis ranging from traditional statistical methods to data mining approaches. Bagging and boosting are two relatively new but popular methods for producing ensembles. In this work, bagging is evaluated on real and benchmark data sets of intrusion detection, direct marketing, and signature verification in conjunction with radial basis function classifier as the base learner. The proposed bagged radial basis function is superior to individual approach for data mining applications in terms of classification accuracy.