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Evaluating a novel or enhanced algorithm is compulsory in data mining studies in order to measure it has superior performance than its previous version. In practice, most of studies apply a straightforward approach for evaluation where appropriate performance metrics such as classification accuracy is selected, computes the mean and its variance over several repetitive experiments, and then compares it with the base algorithm or other comparative approach. However, there are limitations usingdoi:10.18517/ijaseit.7.1.1743 fatcat:ihf74svdgbc3pchtnyxwmppvhq