Classification Models: Non Evolutionary vs. Evolutionary Approach

Data mining has been the active area of research in the last decade. The classification is one of the important task of data mining. Different kind of classification algorithms have been suggested and tested to predict the future events based on unseen data. The objective of this paper is to compare various classification models that have been frequently used in data mining. Three decision trees; one neural network, one statistical and one clustering algorithm are compared on four datasets in terms of predictive accuracy, comprehensibility and training time. Further evolutionary approach based classification model is tested and compared with non evolutionary based approach. Experimental results demonstrate that overall decision tree algorithms are better in terms of accuracy, comprehensibility and training time. Out of the decision trees, QUEST generates trees with lesser levels and depth showing more comprehensibility. Finally GA based classification model shows better performance in context of some parameter including predictive accuracy and comprehensibility.