Improving Accuracy Metric with Precision and Recall Metrics for Optimizing Stochastic Classifier

All stochastic classifiers attempt to improve their classifica-tion performance by constructing an optimized classifier. Typically, all of stochastic classification algorithms employ accuracy metric to discriminate an optimal solution. However, the use of accuracy metric could lead the so-lution towards the sub-optimal solution due less discriminating power. Moreover, the accuracy metric also unable to perform optimally when deal-ing with imbalanced class distribution. In this study, we propose a new evaluation metric that combines accuracy metric with the extended precision and recall metrics to negate these detrimental effects. We refer the new evaluation metric as optimized accuracy with recall-precision (OARP). This paper demonstrates that the OARP metric is more discriminating than the accuracy metric and able to perform optimally when dealing with imba-lanced class distribution using one simple counter-example. We also dem-onstrate empirically that a naïve stochastic classification algorithm, which is Monte Carlo Sampling (MCS) algorithm trained with the OARP metric, is able to obtain better predictive results than the one trained with the accuracy and F-Measure metrics. Additionally, the t-test analysis also shows a clear advantage of the MCS model trained with the OARP metric over the two se-lected metrics for almost five medical data sets