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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In order to assess the suggested algorithm, the MMTS has been benchmarked with several popular algorithms: The classification accuracy depends on both the classifier and the data types. Computational Intelligence and Neuroscience. Unfortunately, the MTS suffers from the lack of a systematic rigorous method for determining the threshold to discriminate between the two classes.
The maximum Fishers Discriminant Ratio -ratio is also considered as a major factor in classifier performance degradation.
Support Vector Machines SVMs showed good classification results for slightly imbalanced data [ 15 ], while for highly mahalaanobis data researchers [ 1617 ] reported poor performance classification results, since SVM try to reduce total error, tagudhi will produce results shifted towards the negative majority class.
MTS approach uses orthogonal array OA experiments to screen the important features. The currently used approaches either are difficult to use in practice such as the loss function [ 36 ] due to the difficulty in evaluating the cost in each case or are based on previously assumed parameters [ 6 ]. If the number of the predetermined classes is tagguchi than two, it is a multiclass classification problem; otherwise, the problem is known as the binary classification problem.
It has been noticed that the effect of the maximum Fishers Discriminant Ratio -ratio is dominated by the imbalance ratio IR effect i.
The most appropriate hyperplane means the one with the largest width of the margin parallel to the hyperplane with no interior points. Unfortunately one of the bit falls for using this approach is that it can be computationally expensive [ 30 ].
To handle the imbalance data, there are proposals such as using penalty constants for different classes found in Lin et al. To achieve this objective of using the optimum number of tagguchi welds that sustain the required strength of the structure, weld quality must be achieved. Finally, MMTS was the least performance among the classifiers for the car dataset. The aim of this work is to enhance the Mahalanobis Taguchi System MTS classifier performance by providing a scientific, rigorous, and systematic method using the ROC curve for determining the threshold that discriminates between the classes.
Modified Mahalanobis Taguchi System for Imbalance Data Classification
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. Table of Contents Alerts. 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.
In this paper, we investigated a wide range of IR, from 1. To achieve an acceptable weld quality, nondestructive weld assessment should be performed. Accordingly, the optimization model becomes.
Using the negative observations only, reference Mahalanobis Distances are calculated using 1 with all features used. Now, after providing faguchi 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 syetem. Jugulum, The Mahalanobis-Taguchi Strategy: The curve drawn in the figure represents the MTS classifier performance for different threshold values.
The essential classifier performance can be explained by examining the confusion matrix Table 1. The algorithmic level approach has been used in many popular classifiers such as decision trees, Support Vector Machines SVMsassociation rule mining, back-propagation BP neural network, one-sample learning, active learning methods, and the Mahalanobis Taguchi System MTS. On the other hand, specificity mahalaanobis be understood as the accuracy of the negative observations: In this context, it can be seen that accuracy and error rate metrics are biased towards one class on behalf of the other.
Imbalance data occurs often in real life such as text classification [ 7 ]. 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. The results showed that the MMTS has a superior performance for high imbalance ratio i.
Changing the threshold haguchi change the point location on the curve i. Accordingly, the datasets were selected related to this criterion i. For the genetic algorithm, the following parameters were used in the implementation: For future research work, the problems of multiclass imbalanced data and the mixed data need to be tackled thoroughly.
Computational Intelligence and Neuroscience
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. The ratio between negative to positive observations left to right columns in Table 1 is representation for the class distribution i. In this section, the description of the dataset used in this study, brief of the used benchmarked classifiers, an overview of the metrics used for imbalanced data classifiers, and the results of classifiers performance for different datasets will be presented.
The closer the classifier performance to this point is, the better it is. Algorithmic level approach solutions are based upon creating a biased algorithm towards positive class. For the low imbalance ratiothe SVM was the best among the classifiers. MTS is a multivariate supervised learning approach, which aims to classify new observation into one of the two classes i. In order to rank the classifiers, the pairwise Mann—Whitney test is used. Many published works [ 16195354 ] pointed out that SVMs classification performance drops significantly when dealing with the imbalance data; therefore, modified SVMs classifiers have been suggested to overcome this issue at both data and algorithmic levels.
For example, let be the cost of wrongly classifying positive instant as a negative one, while is the cost of the contrary case. If the stopping criteria i.
View at Google Scholar N. Tip dressing was performed 10 times approximately every welds in order to return the electrode tip to its original diameter by removing the excess material. In this section, an overview of the benchmarked classifiers, with their parameters, and the machine specifications used for analysis will be presented.
At present, these txguchi have found applications in different domains such as mahalanlbis quality [ 1 ] and speech recognition [ 2 ].