The Mahalanobis Taguchi System is a diagnosis and forecasting method for multivariate data. Mahalanobis distance is a measure based on correlations between the variables and different patterns that can be identified and analyzed with respect to a base or reference group. The Mahalanobis Taguchi System is of interest because of its reported accuracy in forecasting small, correlated data sets. Th...

The Mahalanobis Taguchi System (MTS) is considered one of the most promising binary classification algorithms to handle imbalance data. Unfortunately, MTS lacks a method for determining an efficient threshold for the binary classification. In this paper, a nonlinear optimization model is formulated based on minimizing the distance between MTS Receiver Operating Characteristics (ROC) curve and t...

The Mahalanobis Taguchi System (MTS) is a diagnosis and forecasting method for multivariate data. Mahalanobis distance (MD) is a measure based on correlations between the variables and different patterns that can be identified and analyzed with respect to a base or reference group. MTS is of interest because of its reported accuracy in forecasting small, correlated data sets. This is the type o...

Class overlapping is one of the bottlenecks in data mining and pattern recognition, and affects the classification accuracy and generalization ability directly. In Mahalanobis-Taguchi System (MTS), the normal samples are used to construct reference space, while the abnormal samples are used to verify the validity of the reference space. If there is a class overlapping between the normal samples...

This work presents a novel algorithm, the MTS algorithm, which offers the Mahalanobis Taguchi System (MTS) method for parameter selections which are adjusted under a product parameter design. The utility of the algorithm is assessed how individual product parameter dimensions are selected and it can be used to focus on design system (DS) and to identify product architecture dimensions that are ...

This work presents the application of the Mahalanobis–Taguchi System (MTS) to a dimensional problem in the automotive industry. The combinatorial optimization problem of variable selection is solved by the application of a recent version of binary ant colony optimization algorithm. Moreover, a comparison with respect to binary particle swarm optimization algorithm is also presented and a discus...

The Mahalanobis-Taguchi System (MTS) is a relatively new collection of methods proposed for diagnosis and forecasting using multivariate data. It consists of two main parts: Part 1, the selection of useful variables in order to reduce the complexity of multi-dimensional systems and part 2, diagnosis and prediction, which are used to predict the abnormal group according to the remaining us...

The Mahalanobis-Taguchi System (MTS) is a data mining method employing Mahalanobis distance (MD) and Taguchi′s Robust Engineering philosophy to explore and exploit data in a multidimensional system. The MD calculation provides a measurement scale to discriminate sample data and gives an approach of measuring the level of severity among them. One unique feature of MTS lies its robustness to asse...

the organizations use all appropriate tools to improve their service to the customers. the detection of especial customers and the forecast of their behavior undoubtedly can play an important role in improvement of service. in this paper, a new statistical method called the mahalanobis taguchi system has been used for this purpose. this method is used for the analysis of real data of an insuran...

The current research presents a methodology for classification based on Mahalanobis Distance (MD) and Association Mining using Rough Sets Theory (RST). MD has been used in Mahalanobis Taguchi System (MTS) to develop classification scheme for systems having dichotomous states or categories. In MTS, selection of important features or variables to improve classification accuracy is done using Sign...