ABSTRACT

Feature Selection is an important technique for classification for reducing the dimensionality of feature space and it removes redundant, irrelevant, or noisy data. In this paper the feature are selected based on the ranking methods. (1) Information Gain (IG) attribute evaluation, (2) Gain Ratio (GR) attribute evaluation, (3) Symmetrical Uncertainty (SU) attribute evaluation. This paper evaluates the features which are derived from the 3 methods using supervised learning algorithms K-Nearest Neighbor and Naïve Bayes. The measures used for the classifier are True Positive, False Positive, Accuracy and they compared between the algorithm for experimental results. we have taken 2 data sets Pima and Wine from UCI Repository database.