Data Mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in databases process.  Classification is a major data mining task.

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. In this research work, new hybrid classification methods are proposed using classifiers in a heterogeneous environment with using voting and stacking mechanisms and their performances are analyzed in terms of error rate and accuracy.

A Classifier ensemble was designed using a k-Nearest Neighbour (k-NN), Radial Basis Function (RBF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM). The feasibility and the benefits of the proposed approaches are demonstrated by means of data sets like intrusion detection in computer networks, direct marketing, signature verification. Experimental results demonstrate that the proposed hybrid methods provide significant improvement of prediction accuracy compared to individual classifiers.