All stochastic classifiers attempt to improve their classification performance by constructing an optimized classifier.Typically, all ofudstochastic classification algorithms employ accuracy metric to discriminate an optimal solution.However, the use of accuracy metric could lead the solution towards the sub-optimal solution due less discriminating power.Moreover, the accuracy metric also unable to perform optimally when dealing 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 evaluationudmetric as optimized accuracy with recall-precision (OARP). This paper demonstrates that the OARP metric is more discriminating than the accuracyudmetric and able to perform optimally when dealing with imbalanced class distribution using one simple counter-example.We also demonstrate empirically that a naïve stochastic classification algorithm, which is Monte Carlo Sampling (MCS) algorithm trained with the OARP metric, is able toudobtain better predictive results than the one trained with the accuracy and FMeasure metrics.Additionally, the t-test analysis also shows a clear advantage of the MCS model trained with the OARP metric over the two selected metrics for almost five medical data sets.
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