2014
DOI: 10.1007/s00170-014-6428-9
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A novel approach for classifying imbalance welding data: Mahalanobis genetic algorithm (MGA)

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Cited by 26 publications
(18 citation statements)
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“…MTS is a multivariate supervised learning approach, which aims to classify new observation into one of the two classes (i.e., healthy and unhealthy classes). MTS was used previously in predicting weld quality [ 3 ], exploring the influence of chemicals constitution on hot rolling manufactured products [ 34 ], and selecting the significant features in automotive handling [ 35 ]. The MTS approach starts with collecting considerable observations from the investigated dataset, tailed by separating of the unhealthy dataset (i.e., positive or abnormal) from the healthy (i.e., negative or normal).…”
Section: Literature Reviewmentioning
confidence: 99%
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“…MTS is a multivariate supervised learning approach, which aims to classify new observation into one of the two classes (i.e., healthy and unhealthy classes). MTS was used previously in predicting weld quality [ 3 ], exploring the influence of chemicals constitution on hot rolling manufactured products [ 34 ], and selecting the significant features in automotive handling [ 35 ]. The MTS approach starts with collecting considerable observations from the investigated dataset, tailed by separating of the unhealthy dataset (i.e., positive or abnormal) from the healthy (i.e., negative or normal).…”
Section: Literature Reviewmentioning
confidence: 99%
“…The other research area in the MTS is related to the modification of the Taguchi method not in the threshold determination. Due to the lack of a statistical foundation [ 37 ] for the Taguchi method, the Mahalanobis Genetic Algorithm (MGA) [ 3 ] and the Mahalanobis Taguchi System using Particle Swarm Optimization (PSO) [ 38 ] have been used. Both the MGA and MTS Particle Swarm Optimization methods deal with the Taguchi system (orthogonal array) part, while the threshold determination still lacks a solid foundation or is hard to be determined in reality.…”
Section: Literature Reviewmentioning
confidence: 99%
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