A novel approach for classifying imbalance welding data: Mahalanobis genetic algorithm (MGA)

Modified Mahalanobis Taguchi System for Imbalance Data Classification

“…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).…”

“…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.…”

“…Running the MMTS and the other benchmarked algorithms, in addition to the Mahalanobis Genetic Algorithm (MGA) [ 3 ] over the welding data, Table 7 shows the results for the 10 repetitions in terms of the following metrics: specificity, sensitivity, precision, G means , and F measure . In addition, the suggested threshold is reported for the MMTS and PMTS algorithms.…”

“… a The null hypothesis H o : Median 1 = Median 2 is tested versus the alternative hypothesis H 1 : Median 1 > Median 2 , at a specified level of significance α = 0.05; b Mahalanobis Genetic Algorithm [ 3 ]. …”

“…The classification accuracy depends on both the classifier and the data types. The classifier types can be categorized according to supervised versus unsupervised learning, linear versus nonlinear hyperplane, and feature selection versus feature extraction based approach [ 3 ]. On the other hand, Sun et al [ 4 ] reported that the parameters affecting the classification are the overlapping between data (i.e., class separability), small sample size, within-class concept (i.e., a single class may consist of various subclasses, which do not necessary have the same size), and the data distribution for each class.…”