Paper Title
Class Label Prediction using Constructed Classifier Model

Abstract
A Classifier model is used to predict the relevant class label value. A general issue is to identify the relevant features from a large dataset. In this proposed work, A Decision Tree Classifier is used to predict the relevant class label value for a given unknown instance. We have used Gain ratio metric to select the best splitting attributes in order to construct the efficient classifier model. The decision tree classifier model is constructed using gain ratio. The constructed classifier model is used to measure the performance using cross validation technique. The performance of the constructed classifier is improved with reduce the training and testing time. Index Terms Decision Tree, Feature Selection, Information Gain, Classifier.