Ensemble Decision Tree Classifier for Breast Cancer Data

Data mining is the process of analyzing large quantities of data and summarizing it into useful information. In medical diagnoses the role of data mining approaches increasing rapidly. Particularly Classification algorithms are very helpful in classifying the data, which is important in decision making process for medical practitioners. Further to enhance the classifier accuracy various pre-processing techniques and ensemble techniques were developed. In this study a hybrid approach, CART classifier with feature selection and bagging technique has been considered to evaluate the performance in terms of accuracy and time for classification of various breast cancer datasets.