Comparative Analysis of Various Decision Tree Classification Algorithms using WEKA

Abstract

Classification is a technique to construct a function or set of functions to predict the class of instances whose class label is not known. Discovered knowledge is usually presented in the form of high level, easy to understand classification rules. There is various classification techniques used to classify the data, one of which is decision tree algorithms. This paper presents a comparative analysis of various decision tree based classification algorithms. In experiments, the effectiveness of algorithms is evaluated by comparing the results on 5 datasets from the UCI and KEEL repository.