Selection of Accurate and Robust Classification Model for Binary Classification Problems

Abstract

In this paper we aim to investigate the trade off in selection of an accurate, robust and cost-effective classification model for binary classification problem. With empirical observation we present the evaluation of one-class and two-class classification model. We have experimented with four two-class and one-class classifier models on five UCI datasets. We have evaluated the classification models with Receiver Operating Curve (ROC), Cross validation Error and pair-wise measure Q statistics. Our finding is that in the presence of large amount of relevant training data the two-class classifiers perform better than one-class classifiers for binary classification problem. It is due to the ability of the two class classifier to use negative data samples in its decision. In scenarios when sufficient training data is not available the one-class classification model performs better.