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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Table 5 shows that the MMTS classification results for the examined datasets have the highest values comparable with the others. In order to rank the classifiers, the pairwise Mann—Whitney test is used.
Unfortunately, the MTS suffers from the lack of a mahalahobis rigorous method for determining the threshold to discriminate between the two classes. Therefore, the problem of finding the optimum threshold can be reformulated into the problem of finding the closest point that lies on the curve to point.
In that sense, any performance metrics using both columns will be sensitive to the imbalance data issue, such as accuracy and error rate, 14 and 15respectively. Permanent address of Mahmoud El-Banna is as follows: It includes the following types: The classification accuracy depends on both the mahalanobjs and the data types. For the low imbalance ratiothe SVM was the best among the classifiers.
Literature Review In this section, an overview of the imbalance classification approaches, the Mahalanobis Taguchi System concept, its different areas of applications, weakness points, and its variants is presented.
It should be noted in this study that the imbalance ratio effect on the classification results should be explored. In order to demonstrate the MTS threshold determination mathematically, let us assume that negative data also called healthy or normal observations and the positive data also called unhealthy or abnormal observations are available, where the number of positive observations is and the number of negative observations isand both positive and negative observations consist of variables or features.
In this stage, the optimum threshold and the associated features are determined from the previous stage and the Mahalanobis Distance for the new observation is calculated based on those parameters.
The welding data, summarized in Table 6are used for this case having similar conditions to the one used in El-Banna et al. In this section, an overview of the imbalance classification approaches, the Mahalanobis Taguchi System concept, its different areas of applications, weakness yaguchi, and its variants is presented.
Computational Intelligence and Neuroscience
Watson Research Division As mentioned before, will be used as the main metric, but the results for other metrics will be reported here for future researchers to use. The problem of treating the applications that have imbalance data with the sysrem classifiers leads to bias in the classification accuracy i.
This assessment tagucih be translated into the problem of classifying the dynamic resistance profile input signal for those welds into normal or abnormal welds. Running the MMTS systfm 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: It is worth noticing that a continuous scale is constructed from the single class observations by using MTS; unlike other classification techniques, learning is done directly from the positive and negative observations.
Unfortunately, the examination of accuracy and error rates 14 and 15 reveals that these metrics are not sensitive to the data distribution [ 10 ]. Even with such unrealistic assumption, Naive Bayes still found noticeable success stories comparable with other types of sophisticated classifiers, for example, NB used in text classification [ 47 taguchhi, medical diagnosis [ 48 ], and systems performance management [ 49 ].
Modified Mahalanobis Taguchi System for Imbalance Data Classification
The other research area in the MTS is related to the modification of the Taguchi method not in the threshold determination. Table of Contents Alerts.
Step 1 construction of the initial model stage. To handle the classification of imbalanced data problem, the research community uses data and algorithmic or both approaches.
Classification performance results for the modified Naive Bayes classifiers. Computational Intelligence and Neuroscience. Calculation of the Mahalanobis Distance MD using the negative observation is performed first, followed by scaling i.
This characteristic helps the MTS classifier to deal with the imbalance data problems. Selection of the new features is performed by using mahalahobis orthogonal array approach; then a recalculation of MDs for the negative and the positive observation is performed.
At present, these problems have found applications in different domains such as product quality [ 1 ] and speech recognition [ 2 ]. In this context, it can be seen that accuracy and error rate metrics are biased towards one class on behalf of the other. While at the algorithmic approach, the main idea is to syystem the classier algorithms towards the small class, a combination of the data and algorithmic levels approaches is also used and known as cost-sensitive learning solutions.
The most appropriate hyperplane means the one with the largest width of the margin parallel to the hyperplane with no interior points. The following optimization model is used to determine the optimum threshold that discriminates between the negative and the positive observations, depending on minimizing the Cartesian distance between the MMTS ROC classifier curve and the theoretical optimum point i.
Recently worldwide competition pushes automotive OEMs to ssystem their productivity, reduce nonvalue added activity, and reduce cost. Since uses the right column in the confusion matrix and uses the left syxtem in the confusion matrix, they are unaffected by the imbalance sysem problem.
Support Vector Machines SVMs showed good classification results for slightly mahslanobis data [ 15 mahxlanobis, 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. Several metrics such as accuracy 14error 15specificity 16precision 17sensitivity or recall 18faguchiand 20 are used by the research community as comprehensive assessments of classifiers performances.
A real life case study on manufacturing sector is used to demonstrate the applicability of the proposed model and to compare its performance with Mahalanobis Genetic Algorithm MGA. Now, after providing an overview of how MMTS algorithm works, detailed calculation of the Mahalanobis Distance, the true positive and the negative rates, and the fitness function will be presented in the followings subsection.
The problem with the algorithmic approach is that it needs an extensive knowledge of specific classifier i. The proposed model, Algorithm 1provides an easy, reliable, and systematic way to determine the threshold for the Mahalanobis Taguchi System MTS and its variants i.
Summary of the classifiers performance ranks for all datasets. 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. In imbalance data classification, usually, the revealing of the positive instant is more important than the negative one; hence, the cost of positive instance misclassification outweighs the cost of negatives ones i.
Therefore, autoindustry is extremely concerned with the elimination of these redundant welds.