Artificial bee colony optimisation-based enhanced Mahalanobis Taguchi system for classification

Iquebal, Ashif Sikandar; Pal, Avishek

doi:10.1504/ijiei.2014.066217

Cited by 4 publications

(6 citation statements)

References 16 publications

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“…The results in terms of the total misclassification error and the AUC metrics showed that the MGA had better classification performance than MTS Welding Iquebal and Pal (2015) Dimensional reduction This research explores the application of artificial bee colony (ABC) optimization to select features in MTS. The optimal subset of features is obtained via the stochastic search mechanism of binary ABC.…”

Section: Mts Area Short Description and Resultsmentioning

confidence: 99%

Mahalanobis-Taguchi system: state of the art

Mota-Gutiérrez

Reséndiz‐Flores

Reyes-Carlos

2018

IJQRM

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Purpose The purpose of this paper is to show a bibliographical review of the applications of the MTS throughout the time and the different fields. Design/methodology/approach The Mahalanobis-Taguchi system (MTS) is an analytical method used for the diagnosis and/or pattern recognition of multivariate data for quantitative decision making. Findings Its scope is very broad, ranging from engineering, medicine, education, and manufacturing, among others. This work presents a classification of the literature in the following areas of the MTS: introduction of the method, cases of study/application, comparison with other methods, integration and development of the MTS with other methods, construction of Mahalanobis space, dimensional reduction and threshold establishment. It realized a wide search of the publications in magazines and congresses. Originality/value This paper is a summary of the main applications, contributions and changes to MTS.

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Section: Mts Area Short Description and Resultsmentioning

confidence: 99%

Mahalanobis-Taguchi system: state of the art

Mota-Gutiérrez

Reséndiz‐Flores

Reyes-Carlos

2018

IJQRM

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“…Due to minimal resources within the T-Method itself, the authors are relying on the progress of OA enhancement within the MT-Method for practical purposes. The most recent study conducted by Mota-Gutiérrez et al [16] which summarised the 18 year progress of the MTmethod in various industrial practices was found to be very helpful for this study since the area of dimensional reduction or variables optimization by several researchers are still being progressively enhanced till recently [7], [17][18]. This study provides a conceptual idea in applying the same concept to T-Method variable screening or optimization since very few studies have been conducted within that area.…”

Section: Related Studiesmentioning

confidence: 92%

“…Iquebal et al [7] compared MT-ABC, MT-Particle Swarm Optimization (PSO), and MT-Genetic Algorithm (GA) in their study on several benchmark datasets and MT-PSO was found to be the best in terms of running time with very minimal differences in terms of its accuracy compared to MT-ABC. Several other studies whether within MTS or beyond the MTS nature, also highlighted that Binary PSO converges faster compared to other algorithms.…”

Section: Introductionmentioning

confidence: 99%

Binary Particle Swarm Optimization for Variables Selection Optimization in Taguchi’s T-Method

et al. 2020

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Prediction analysis has drawn significant interest in numerous field. Taguchi’s T-Method is a prediction tool that developed practically but not limited to small sample analysis. It was developed explicitly for multidimensional system prediction by relying on historical data as the baseline model and adapting the signal to noise ratio (SNR) as well as zero proportional concepts in strengthening its robustness. Orthogonal array (OA) in T-Method is a variable selection optimization technique in improving the prediction accuracy as well as help in eliminating variables that may deteriorate the overall performance. However, the limitation of OA in dealing with higher multidimensionality restraint the optimization accuracy. Binary particle swarm optimization used in this study helps to cater to the limitation of OA as well as optimizing the variable selection process to better prediction accuracy. The results show that if the historical data consist of samples with higher correlation of determination (R2) value for the model creation, the optimization process in reducing the number of variables would be much reliable and accurate. Comparing between T-Method+OA and T-Method+BPSO in four different case study, it shows that T-Method+BPSO performing better with greater R2 and means relative error (MRE) value compared to T-Method+OA.

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“…MTS has been the most promising method for using binary classification to deal with uneven data [16]. MTS put objects into two groups by using MD-based cutoff values [19]. MD changed the shape of the super pixels to better fit the changing structure of the real world [20], figured out the right way to find the value of a random sample [21], put people on the outside of the study based on a basic regression study [22], got an indicator of the tool's health [23], and gave more accurate results for the right decision-making process [24].…”

Section: Introductionmentioning

confidence: 99%

Methadone Flexi Dispensing (MFlex) Intelligence System utilizing the Mahalanobis-Taguchi System

Pinueh,

Abu,

Saad

et al. 2024

JMMST

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Patients who are participating in the methadone flexi dispensing (MFlex) program are obliged to provide their blood samples for various testing, such as lipid profiles. A doctor evaluates three parameters, including cholesterol, HDL cholesterol, and LDL cholesterol to determine whether or not the patient has a lipid issue. Since, the current structure lacks an ideal atmosphere for classification and optimization caused by inaccuracies in measurement methodologies and a lack of explanation for significant aspects that have an effect on the accuracy of diagnostics. The objective is to implement the Mahalanobis-Taguchi system (MTS) in the MFlex program. Utilizing a total of 34 parameters, there are two different types of MTS techniques used for classification and optimization: the RT method and T method. The average Mahalanobis distance (MD) for healthy conditions is 1.0000 whereas for unhealthy is 79.5876. As a result, there is 19 parameters indicate a positive degree of contribution. 15 unknown samples were diagnosed with a variety of positive and negative degree of contribution to achieving a lower MD. Type 5 of 6 alterations was chosen as the best suggested possibility. In conclusion, MTS is able to be applied in medical environment.

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Artificial bee colony optimisation-based enhanced Mahalanobis Taguchi system for classification

Cited by 4 publications

References 16 publications

Mahalanobis-Taguchi system: state of the art

Mahalanobis-Taguchi system: state of the art

Binary Particle Swarm Optimization for Variables Selection Optimization in Taguchi’s T-Method

Methadone Flexi Dispensing (MFlex) Intelligence System utilizing the Mahalanobis-Taguchi System

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