| Summary: | This paper aimed to determine the efficiency of classifiers for highdimensional classification methods. It also investigated whether an extreme minimum misclassification rate translates into robust efficiency. To ensure an acceptable procedure, a benchmark evaluation threshold (BETH) was proposed as a metric to analyze the comparativ AB performance for high-dimensional classification methods. A simplified performance metric (ω) was derived to show the efficiency of different classification methods. To achieve the objectives, the existing probability of correct classification (PCC) or classification accuracy reported in five different articles was used to generate the BETH value. Then, a comparative analysis was performed between the application of BETH value (ϕ)and the well-established PCC value (∂), derived from the confusion matrix. The analysis indicated that th I N RODU procedure had a minimum misclassification rate, unlike the Optimal method. The results also revealed that as the PCC inclined toward unity value, the misclassification rate between the two methods (BETH an PCC) became extremely irrelevant. The study revealed th at the BETH method was inv ariont to th e performance established by the classifiers using the PCC criterion but demonstrated more relevant aspects of robustness and mininmm misclassification rate as compared to the PCC method. In addition, the comparative analysis affirmed that the BETH method exhibited more robust efficiency than the Optimal method. The study concluded that a minimum misclassification rate yields robust performance efficiency. © 2022. All Rights Reserved.
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