An empirical study on several classification algorithms and their improvements

Abstract

Classification algorithms as an important technology in data mining and machine learning have been widely studied and applied. Many methods can be used to build classifiers, such as the decision tree, Bayesian method, instance-based learning, artificial neural network and support vector machine. This paper focuses on the classification methods based on decision tree learning, Bayesian learning, and instance-based learning. In each kind of classification methods, many improvements have been presented to scale up the classification accuracy of the basic algorithm. The paper also studies and compares the classification performance on classification accuracy empirically, using the whole 36 UCI data sets obtained from various sources selected by Weka. The experiment results re-demonstrate the efficiency of all these improved algorithms. © Springer-Verlag 2009.