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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The problem of treating the applications that have imbalance data with the common classifiers leads to bias in the classification accuracy i.
Summary of the classifiers performance ranks for all datasets. Changing the threshold will change the point location on the curve i. On the other hand, Sun et al. Computational Intelligence and Neuroscience.
As shown in Figure 1pointrepresents the optimum theoretical solution best performance for any classifier. It is worth mentioning that no tuning parameters for any of the examined classifiers were performed; consequently, baseline line comparisons among the classifiers with the default setting were established, which leads to the most mhaalanobis classifier selection [ 50 ].
In this stage, the optimum threshold and the associated features mahalanobiis determined from the previous stage and the Mahalanobis Distance for the new observation is calculated based on those parameters. To determine the appropriate threshold, loss function approach was proposed by [ 36 ]; however, it is not a practical approach because of the difficulty in specifying the relative cost [ 37 ].
The organization of the paper is as follows: In this context, it can be seen that accuracy and error rate metrics are biased towards one class on behalf of the other.
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 metric, but still biased to one class [ 10 ].
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.
The maximum Fishers Discriminant Ratio -ratio is also considered as a major factor in classifier performance degradation. Section 5 presents a case study to demonstrate the applicability of the proposed research.
Mathematically, this can be converted into the following optimization model. Accordingly, new features will be ststem using the orthogonal array approach, and true positive rate, false positive rate, and the fitness function will be also updated. 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.
View at Taguchj B. To handle the imbalance data, there are proposals such as using penalty constants for different classes found in Taguhci et al. MTS is a multivariate supervised learning approach, which aims to classify new observation into one of the two classes i.
In order to assess the suggested algorithm, the MMTS has been benchmarked with several popular algorithms: Subscribe to Table of Contents Alerts.
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. Systm on active learning for imbalance data reported by Ertekin et al.
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.
Unfortunately, the examination of accuracy and error rates 14 and 15 reveals that these metrics are not sensitive to the data distribution [ 10 ]. Watson Research Division The Mahalanobis Taguchi System MTS is one of the most promising binary classification approaches to handling the imbalance data problem. Algorithmic level approach solutions are based upon creating a biased algorithm towards positive class. Solutions to deal with the imbalanced learning problem can be summarized into the following approaches [ 10 ]: View at Scopus N.
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 ].
The curve drawn in the figure represents the MTS classifier performance for different threshold values. A low value of -ratio means that observations are mixed together and overlapped regions are large, and therefore it is difficult to discriminate between these observations.
The experimental setup, the materials used, and all the other related information can be found in the same reference. Despite the above-mentioned advantages, weld quality cannot be estimated with high certainty due to factors such as tip wear, sheet metal debris, variation in the power supply; therefore, it is common practice in the autoindustry to add extra taaguchi to increase their confidence in the structural integrity of the welded assembly [ 40 ].