Comparative Study of Decision Tree Classifier with and without GA based feature selection

Abstract: Machine Learning techniques like Genetic Algorithms and decision trees have been applied to the field of classification for more than a decade. It can learn normal and anomalous patterns from training data and generate classifiers, which can be used to classify samples of unknown class. In general, the input data to classifiers is an extremely large set of features, but not all of features are relevant to the classes to be classified. Hence, the learner must generalize from the given examples in order to produce a useful output in new cases. In this paper, a comparison of decision tree with Genetic Algorithm based feature selection and a decision tree without Genetic Algorithm is carried out on different datasets.

Keywords: Decision Tree, Genetic Algorithm, ID3.