Performance Evaluation of Classification Techniques based on Accuracy of Results

Data mining techniques, especially classification methods, are receiving increasing attention from researchers and practitioners. Classification is a data mining (machine learning) technique used to predict group membership for data instances. This article is aimed at evaluating the performance of different classification methods based on the parameter “accuracy of results” of the classification method. Classification methods covered in the study include Decision Trees, Nearest Neighbors algorithm, Bayesian Networks and Support Vector Machines. To render more credibility to the results, the target algorithms have been tested on four datasets taken from UCI Machine Learning Repository.