The Mahalanobis Taguchi System (MTS) is considered one of the most promising binary classification algorithms to handle\nimbalance data. Unfortunately, MTS lacks a method for determining an efficient threshold for the binary classification. In\nthis paper, a nonlinear optimization model is formulated based on minimizing the distance between MTS Receiver Operating\nCharacteristics (ROC) curve and the theoretical optimal point named Modified Mahalanobis Taguchi System (MMTS). To\nvalidate the MMTS classification efficacy, it has been benchmarked with Support Vector Machines (SVMs), Naive Bayes (NB),\nProbabilistic Mahalanobis Taguchi Systems (PTM), Synthetic Minority Oversampling Technique (SMOTE), Adaptive Conformal\nTransformation (ACT), Kernel Boundary Alignment (KBA), Hidden Naive Bayes (HNB), and other improved Naive Bayes\nalgorithms. MMTS outperforms the benchmarked algorithms especially when the imbalance ratio is greater than 400. A real life\ncase study on manufacturing sector is used to demonstrate the applicability of the proposed model and to compare its performance\nwith Mahalanobis Genetic Algorithm (MGA).
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