Evaluation of Decision Tree Classifiers on Tumor Datasets

Abstract

Abstract: Classification is playing an important role in the field of data mining as well as in the studies of machine learning, statistics, neural networks and many expert systems over years. Different classification algorithm has been successfully implemented in various applications. Among them some of the popular implications of classification algorithms are scientific experiments, image processing, fraud detection, medical diagnosis and lots more. In the recent years medical data classification especially tumor data classification caught a huge interest among the researchers. Decision tree classifiers are used extensively for different types of tumor cases. In this paper, performance of decision tree induction algorithms on tumor medical data sets in terms of Accuracy and time complexities are analyzed.