Modified Mahalanobis Taguchi System for Imbalance Data Classification

El-Banna, Mahmoud

doi:10.1155/2017/5874896

Cited by 29 publications

(22 citation statements)

References 42 publications

(65 reference 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%

Multiclass Model for Agriculture Development Using Multivariate Statistical Method

et al. 2020

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Mahalanobis taguchi system (MTS) is a multi-variate statistical method extensively used for feature selection and binary classification problems. The calculation of orthogonal array and signalto-noise ratio in MTS makes the algorithm complicated when more number of factors are involved in the classification problem. Also the decision is based on the accuracy of normal and abnormal observations of the dataset. In this paper, a multiclass model using Improved Mahalanobis Taguchi System (IMTS) is proposed based on normal observations and Mahalanobis distance for agriculture development. Twentysix input factors relevant to crop cultivation have been identified and clustered into six main factors for the development of the model. The multiclass model is developed with the consideration of the relative importance of the factors. An objective function is defined for the classification of three crops, namely paddy, sugarcane and groundnut. The classification results are verified against the results obtained from the agriculture experts working in the field. The proposed classifier provides 100% accuracy, recall, precision and 0% error rate when compared with other traditional classifier models.

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Section: Related Workmentioning

confidence: 99%

Multiclass Model for Agriculture Development Using Multivariate Statistical Method

et al. 2020

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“…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%

Binary Bitwise Artificial Bee Colony as Feature Selection Optimization Approach within Taguchi’s T-Method

Harudin

Ramlie

Muhamad

et al. 2021

Mathematical Problems in Engineering

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Taguchi’s T-Method is one of the Mahalanobis Taguchi System- (MTS-) ruled prediction techniques that has been established specifically but not limited to small, multivariate sample data. The prediction model’s complexity aspect can be further enhanced by removing features that do not provide valuable information on the overall prediction. In order to accomplish this, a matrix called orthogonal array (OA) is used within the existing Taguchi’s T-Method. However, OA’s fixed-scheme matrix and its drawback in coping with the high-dimensionality factor led to a suboptimal solution. On the contrary, the usage of SNR (dB) as its objective function was a reliable measure. The application of Binary Bitwise Artificial Bee Colony (BitABC) has been adopted as the novel search engine that helps cater to OA’s limitation within Taguchi’s T-Method. The generalization aspect using bootstrap was a fundamental addition incorporated in this research to control the effect of overfitting in the analysis. The adoption of BitABC has been tested on eight (8) case studies, including large and small sample datasets. The result shows improved predictive accuracy ranging between 13.99% and 32.86% depending on cases. This study proved that incorporating BitABC techniques into Taguchi’s T-Method methodology effectively improved its prediction accuracy.

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“…In addition to the enhancement of the feature selection phase, there is also a considerable deal of effort on decision boundary (threshold) determination. 16,29–31 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%

Quality inspection of complex-shaped metal parts by vibrations and an integrated Mahalanobis classification system

Cheng

Yaghoubi

Paepegem

et al. 2020

Structural Health Monitoring

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The Mahalanobis–Taguchi system is considered as a promising and powerful tool for handling binary classification cases. Though, the Mahalanobis–Taguchi system has several restrictions in screening useful features and determining the decision boundary in an optimal manner. In this article, an integrated Mahalanobis classification system is proposed which builds on the concept of Mahalanobis distance and its space. The integrated Mahalanobis classification system integrates the decision boundary searching process, based on particle swarm optimizer, directly into the feature selection phase for constructing the Mahalanobis distance space. This integration (a) avoids the need for user-dependent input parameters and (b) improves the classification performance. For the feature selection phase, both the use of binary particle swarm optimizer and binary gravitational search algorithm is investigated. To deal with possible overfitting problems in case of sparse data sets, k-fold cross-validation is considered. The integrated Mahalanobis classification system procedure is benchmarked with the classical Mahalanobis–Taguchi system as well as the recently proposed two-stage Mahalanobis classification system in terms of classification performance. Results are presented on both an experimental case study of complex-shaped metallic turbine blades with various damage types and a synthetic case study of cylindrical dogbone samples with creep and microstructural damage. The results indicate that the proposed integrated Mahalanobis classification system shows good and robust classification performance.

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Modified Mahalanobis Taguchi System for Imbalance Data Classification

Cited by 29 publications

References 42 publications

Multiclass Model for Agriculture Development Using Multivariate Statistical Method

Multiclass Model for Agriculture Development Using Multivariate Statistical Method

Binary Bitwise Artificial Bee Colony as Feature Selection Optimization Approach within Taguchi’s T-Method

Quality inspection of complex-shaped metal parts by vibrations and an integrated Mahalanobis classification system

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