2017
DOI: 10.1155/2017/5874896
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Modified Mahalanobis Taguchi System for Imbalance Data Classification

Abstract: The Mahalanobis Taguchi System (MTS) is considered one of the most promising binary classification algorithms to handle imbalance data. Unfortunately, MTS lacks a method for determining an efficient threshold for the binary classification. In this paper, a nonlinear optimization model is formulated based on minimizing the distance between MTS Receiver Operating Characteristics (ROC) curve and the theoretical optimal point named Modified Mahalanobis Taguchi System (MMTS). To validate the MMTS classification eff… Show more

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Cited by 29 publications
(22 citation statements)
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“…An optimized binary classification was developed using a modified MTS(MMTS) method. The MMTS showed better results compared with the results obtained from Support Vector Machine(SVM), Probabilistic MTS, Naive Bayes, Hidden Naive Bayes, Kernel Boundary Alignment, Adaptive Conformal Transformation and Synthetic Minority Oversampling Technique methods [24]- [26]. A novel method was developed for the identification of conditions of roads using MTS, where it was applied to classify the quality of roads in cities [27].…”
Section: Related Workmentioning
confidence: 99%
“…An optimized binary classification was developed using a modified MTS(MMTS) method. The MMTS showed better results compared with the results obtained from Support Vector Machine(SVM), Probabilistic MTS, Naive Bayes, Hidden Naive Bayes, Kernel Boundary Alignment, Adaptive Conformal Transformation and Synthetic Minority Oversampling Technique methods [24]- [26]. A novel method was developed for the identification of conditions of roads using MTS, where it was applied to classify the quality of roads in cities [27].…”
Section: Related Workmentioning
confidence: 99%
“…MTS establishes a multivariate measurement scale that recognizes a normal or healthy observation from an abnormal or an unhealthy observation and integrates it with the concept of signal-to-noise ratio (SNR) and orthogonal array (OA). Beginning with the introduction of the MT-Method as a classification technique that has so far gained much attention among scholars [7][8][9][10][11][12][13][14], Taguchi's T-Method has been established since then, which has utilized the same integration principles. e unit-space concept, the duplicate signal-to-noise ratio (SNR) adaptation as a weighting factor, zero-proportional theory, and OA as the feature selection optimization are the main elements that have been adopted in reinforcing Taguchi's T-Method robustness.…”
Section: Introductionmentioning
confidence: 99%
“…In addition to the enhancement of the feature selection phase, there is also a considerable deal of effort on decision boundary (threshold) determination. 16,2931 In Taguchi and Wu, 29 Taguchi proposed a quadratic loss function to determine the classification threshold; however, it is unrealistic in practical uses. A probabilistic approach to establish thresholds to classify the samples as “Healthy” or “Unhealthy” is proposed in Kumar et al 30 A receiver operating characteristic (ROC) curve-based method is consecutively proposed in El-Banna.…”
Section: Introductionmentioning
confidence: 99%