Applying the Mahalanobis–Taguchi System to. Improve Tablet PC Production Processes. Chi-Feng Peng 2,†, Li-Hsing Ho 3,†, Sang-Bing Tsai. The purpose of this paper is to present and analyze the current literature related to developing and improving the Mahalanobis-Taguchi system (MTS) and to. ABSTRACT. The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to.
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Cost-sensitive methods used different costs or penalties for different misclassification types. The case presented will be in the manufacturing sector in the area of resistance spot welding. On the other hand, specificity can be understood as the accuracy of the negative observations: Literature Review In this section, an overview of the imbalance classification approaches, the Mahalanobis Taguchi System concept, its different areas of applications, mahalnaobis points, and its variants is presented.
Computational Intelligence and Neuroscience
While the problem reported [ 4 ] using the algorithmic approach is that it needs a deep understanding about the classier used itself and the application area i. As shown in Figure 1pointrepresents the optimum theoretical solution best performance for any classifier. View at Google Scholar A. Different types of cost-sensitive approaches have been reported in the literature: A rough method for determining the threshold is to plot the positive and negative MD observations versus their orders and decide upon the threshold manually.
Support Vector Machines SVMs showed good classification results for slightly imbalanced data [ 15 ], while for highly imbalanced data researchers [ 1617 ] reported poor performance classification results, since SVM try to reduce total error, which will produce results shifted towards the negative majority class. On the other hand, one-class learning [ 2425 ] used the target class only to determine if the new observation belongs to this class or not.
Association rule mining is a recent classification approach combining association mining and classification into one approach [ 20 — 22 ]. Since more features mean a higher cost of monitoring and require more processing time, it is important to exclude the unnecessary features from having an efficient classifier.
Algorithmic level approach solutions are based upon creating a biased algorithm towards positive class. Finally, the aim of this research is to enhance the Mahalanobis Taguchi System MTS classifier performance through providing a scientific, rigorous, and systematic method of determining the binary classification threshold that discriminates between the two classes, which can be applied to the MTS and its variants i. The next step is to determine the threshold that will be used to discriminate the negative observations from the positive ones based on the MD magnitude, which means that the new observation can be classified into either a positive or negative observation according to the following criteria: Data level approach [ 11 ] is mainly returning the balance distribution between the classes through resampling techniques.
This assumption means that the influence of sustem on a given class is independent of each other.
The first work regarding SVMs was published by Cortes and Vapnik [ 42 ], continued by significant contributions from other researchers [ 43 ]. For the data approach, the main idea is to balance the class density randomly or informatively i.
In order to demonstrate the MMTS applicability, a case study in the welding area was used. Since uses the right column in the confusion matrix and uses the left column in the confusion matrix, they are unaffected by the imbalance data problem.
The MTS approach starts with collecting considerable observations from the investigated dataset, tailed by separating of the unhealthy dataset i.
The most common used metrics for the evaluation of the imbalance data classification performance are andwhere the last one uses weighted importance of the recall and precision controlled bythe default value of is 1which results in better assessment than accuracy systeem, but still biased to one class [ 10 ]. 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: Therefore, will be used as a main metric for the analysis criterion.
The author declares that there are no conflicts of interest regarding the publication of this paper. Based taguchj the above equation, the feature mean gain can be calculated by where is an index that represents the feature,and is the total number of features.
Modified Mahalanobis Taguchi System for Imbalance Data Classification
The other research area in the MTS tguchi related to the modification of the Taguchi method not in the threshold determination. Watson Research Division MMTS and the benchmarked algorithms have been evaluated for each of the ten repetitions simultaneously. View at Google Scholar N.
Table 8 shows the values obtained from comparing the performances of the classifiers between any two classifiers using the Mann—Whitney test and the resulting classifiers rank. Mathematically, this can be converted into the following optimization model.
While data and algorithmic approaches constitute the majority efforts in the area of imbalanced data, several other approaches have also been conducted, which will be raguchi in Literature Review. A set of data is sampled from both classes. The scaled Twguchi for the positive date set supposes to be different from MD for those for the negative dataset. In order to assess the suggested algorithm, the Mayalanobis has been benchmarked with several popular algorithms: It has been shown in [ 6 ] that PTM classifier performance outperformed MTS classifier performance; therefore, it has been selected to be benchmarked with the proposed classifier.
The Mahalanobis Taguchi System MTS is considered one of the most promising binary classification algorithms to handle imbalance data. Imbalance data occurs often in real life such as text classification [ 7 ]. Therefore, in this paper, SVM was selected as one of the benchmarked algorithms to compare with ours; the results showed that SVM classification performance largely degrades with a high imbalance ratio, which supports the previous findings of the researchers more details will be presented in Results.
In the case of highly imbalanced data, one-class learning showed good classification results [ 28 ].
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 ].