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 classification accuracy depends on both the classifier and the data types. Classification is one of the supervised learning approaches in which a new observation needs to be assigned to one of the predetermined classes or categories. 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.
Changing the threshold will change the point location on the curve i. The most important metrics among the above-mentioned ones are the sensitivity and the specificity, whereas the first one sometimes called recall can be seen as the accuracy of the positive observations: The closest point lies on the curve i.
In this paper, a nonlinear optimization model with the objective of minimizing the Euclidean distance between MTS classifier ROC mahalnaobis and the theoretical optimal point i.
Computational Intelligence and Neuroscience
In this paper, we investigated a wide range of IR, from 1. View at Google Scholar I. Table 8 shows the values obtained from comparing the performances of the classifiers between any two classifiers using the Mann—Whitney test and the mahalanobix classifiers rank.
In this context, it can be seen that accuracy and error rate metrics are biased towards one class on behalf of the other. Each factor in the orthogonal array design can be calculated independently of all other factors since the design is balanced i. 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, autoindustry is extremely concerned with the elimination of these redundant welds.
Unfortunately, the examination of accuracy and error rates 14 and 15 reveals that these metrics are not sensitive to the data distribution [ 10 tagucchi. 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. Unfortunately, the PTM method is based on previously assumed parameters, and the accuracy of the classification results was less than the benchmarked classifiers this is tagucho of the findings in this research, which will be discussed in Results.
The Mahalanobis Sysgem System MTS is one of the most promising binary classification approaches to handling the imbalance data problem.
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 ]. It should systek noted in this study mshalanobis the imbalance ratio effect on the classification results should be explored. The contribution of this paper mainly is in the area of establishing a reliable and systematic threshold for classification.
Modified Mahalanobis Taguchi System for Imbalance Data Classification
MMTS outperforms the benchmarked algorithms especially when the imbalance ratio is greater than Permanent address of Mahmoud El-Banna is as follows: The ROC is beneficial because it provides a tool to show the advantages represented by true positives versus disadvantages represented by false positives of the classifier relating to data density.
Knowing the cost matrices in most cases is practically difficult. Both the MGA and MTS Particle Swarm Optimization methods deal with the Taguchi system orthogonal mahalanobsi part, while the threshold determination still lacks a solid foundation or is hard to be determined in reality. 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.
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 welding data, summarized in Table 6are used for this case having similar conditions to the one used in El-Banna et al.
To achieve an acceptable weld quality, nondestructive weld assessment should be performed. 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 mahxlanobis [ 6 ].
Section 2 reviews the previous work of imbalance data classifications methods, the Mahalanobis Taguchi System, and its applications. Based on this table, one can observe the following: Despite the above-mentioned advantages, weld quality cannot be sysgem 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 welds to increase their confidence in the structural integrity of the welded assembly [ 40 ].
Cost-sensitive methods used different costs or penalties for different mahalanobid types. The first step after obtaining the dataset was to split them into training and testing groups.
The essential classifier performance can be explained by examining the confusion matrix Table 1. In order to determine if there is a significant difference among the classifiers performances i. The first work regarding SVMs was published by Cortes and Vapnik [ 42 ], continued by significant contributions from other researchers [ 43 ]. View at Google Scholar S. The problem of treating the applications that have imbalance data with the common classifiers leads to bias in the classification accuracy i.
The assumption of an equal number of observations in each class is elementary in using the common classification methods such as decision tree analysis, Support Vector Machines, discriminant analysis, and neural networks [ 6 ]. The results showed that the MMTS has a superior performance for high imbalance ratio i.
Table 2 contains a description of the selected datasets properties. Jugulum, The Mahalanobis-Taguchi Strategy: MTS is a multivariate supervised learning approach, which aims to classify new observation into one of the two classes i.
The curve drawn in the figure represents the MTS classifier performance for different threshold values. The problem with the algorithmic approach is that it needs an extensive knowledge of specific classifier i. Table of Contents Alerts. This characteristic helps the MTS classifier to deal with the imbalance data problems.
The other research area in the MTS is related to the modification of the Taguchi method not in the threshold determination. Subscribe to Table of Contents Alerts. In order to investigate the robustness performance of the studied classifiers related to the class imbalance criterion, fourteen different UCI [ 41 ] datasets and one data welding from El-Banna et al.
If the stopping criteria are met, then the training stage is done, and the model is ready for testing observations.
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.
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.