Description

The aim of this research is to find out the best classifier with respect to accuracy in detecting breast cancer. The experiments are conducted in WEKA (Waikato Environment for Knowledge Analysis). WEKA is a collection of machine learning algorithms for data mining tasks. In the performance criterion of supervised learning classifiers such as Decision trees and SVM are compared, to find the best classifier in breast cancer datasets (WBC). In the performance of Support Vector Machine (SVM) and Decision Tree are compared to find the best classifier in WBC. SVM proves to be the most accurate classifier with accuracy of 96.99%.In the performance of decision tree classifier with or without feature selection in breast cancer datasets Breast Cancer, WBC. The selected attributes in the dataset are: Uniformity of Cell Size, Mitoses, Clump thickness, Bare Nuclei, Single Epithelial cell size, Marginal adhesion, Bland Chromatin and Class. In this paper we compare the performance of SVM classifier and Decision Tree Classifier. Finally we evaluate the performance based on the resource utilizations of classification and also based on the time efficiency. This paper mainly focuses on comparing the classifier and produce the classifier results and evaluate the performance of individual classifiers.