Increasing T-method accuracy through application of robust M-estimatior

Mahalanobis Taguchi System is an analytical tool involving classification, clustering as well as prediction techniques. T-Method which is part of it is a multivariate analysis technique designed mainly for prediction and optimization purposes. The good things about T-Method is that prediction is always possible even with limited sample size. In applying T-Method, the analyst is advised to clearly understand the trend and states of the data population since this method is good in dealing with limited sample size data but for higher samples or extremely high samples data it might have more things to ponder. T-Method is not being mentioned robust to the effect of outliers within it, so dealing with high sample data will put the prediction accuracy at risk. By incorporating outliers in overall data analysis, it may contribute to a non-normality state beside the entire classical methods breakdown. Considering the risk towards lower prediction accuracy, it is important to consider the risk of lower accuracy for the individual estimates so that the overall prediction accuracy will be increased. Dealing with that intention, there exist several robust parameters estimates such as M-estimator, that able to give good results even with the data contain or may not contain outliers in it. Generalized inverse regression estimator (GIR) also been used in this research as well as Ordinary Lease Square Method (OLS) as part of comparison study. Embedding these methods into T-Method individual estimates conditionally helps in enhancing the accuracy of the T-Method while analyzing the robustness of T-method itself. However, from the 3 main case studies been used within this analysis, it shows that T-Method contributed to a better and acceptable performance with error percentages range 2.5% ~ 22.8% between all cases compared to other methods. M-estimator is proved to be sensitive with data consist of leverage point in x-axis as well as data with limited sample size. Referring to these 3 case studies only, it can be concluded that robust M-estimator is not feasible to be applied into T-Method as of now. Further enhance analysis is needed to encounter issues such as Airfoil noise case study data which T -method contributed to highest error% prediction. Hence further analysis need to be done for better result review

Application of Mahalanobis-Taguchi System in Rainfall Trends at UMP Gambang Campus

Rainfall is a variable meteorological phenomenon that exhibits spatial variability across different locations. Weather stations collect a wide range of parameters to monitor and analyze rainfall patterns. However, not all parameters are equally significant or efficient in performing classification and optimization tasks. In this study, we propose the use of the Mahalanobis-Taguchi system (MTS) method to classify rainfall occurrences by RT-Method and optimize the parameter selection process by T-Method. The data were collected by weather station Vantage Pro2 in UMP Gambang. By applying RT- Method, we can classify the data sample in term of MD for November, May and April while reducing the number of parameters to only those that significantly contribute to the classification, which from 16 parameters to 8 parameters using T-Method. This approach provides a streamlined and efficient methodology for analyzing rainfall patterns and optimizing weather station data collection processes

Application of Mahalanobis-Taguchi system in Rainfall Distribution

The rainfall time series is often nonlinear and multi-time scale because of hydrology, meteorological, and human activity. Weather stations gather information on a diverse set of parameters on order to monitor and analyses patterns of rainfall. Nevertheless, not all parameters are created equal in terms of its significance or effectiveness in carrying out classification and optimization actions. The objective is to classify rainfall occurrences by the RT method and optimize the parameter selection process by the T method using Mahalanobis-Taguchi system (MTS). The data was collected using Vantage Pro2 weather station at UMPSA Gambang campus and it consists of 16 various parameters. As a results, RT method can classify the data samples in terms of MD for the months of June, October and December by utilizing the, while simultaneously the number of parameters is reduced to only those that substantially contribute to the classification. This brings the total number of parameters decrease from 16 to 8 when compared to the T method. So, this research methods offer a simplified and effective way for analyzing rainfall patterns and optimizing the data gathering processes at weather stations

Mahalanobis-Taguchi system for pattern recognition: A brief review

Mahalanobis-Taguchi system (MTS) is a data mining method that employs Mahalanobis distance (MD) and Taguchi robust engineering philosophy, in order to explore and exploit data in a multidimensional system. Furthermore, MD calculation provides a measurement scale that is used to discriminate and interpret the relationships among data samples (abnormal vs normal). Another function of MD calculation is that it is an approach of measuring the level of dissimilarity between them (severity). One unique feature of MTS lies on its precision in assessing the variability among all levels of samples (noise). Besides, MTS possesses the ability to evaluate the significant and insignificant factors that contribute to the performance of system (optimize). This evaluation is performed through a simplistic yet effective technique (orthogonal array and signal to noise ratio). Moreover, MTS receives a tremendous amount of appreciations by various industries due to its ability to perform various functions in diverse industrial applications (among others) of optimization, inspection, diagnostics, monitoring, estimation, prediction, classification and discrimination. Despite the appreciations received by this method, some criticize the reliability of MTS on its effectiveness, while some contribute ingenious ideas (hybrid systems, intelligent search algorithms, novel mathematical computation methods, etc.) for a further improvement of this method’s implementation. Therefore, this paper provides a general review on MTS research trends and studies conducted on this method outside Japan, in order to identify future research prospects

A hybrid methodology for the mahalanobis-taguchi system using random binary search-based feature selection

The Mahalanobis-Taguchi system (MTS) is a relatively new statistical methodology combining various mathematical concepts and is used in the field of diagnosis and classification in multidimensional systems. MTS is a very efficient method and has already been applied to a wide range of disciplines. However, its feature selection phase (optimization stage), which uses experimental designs (orthogonal array, OA), is susceptible to improvement. In MTS, selection of important features or variables to improve classification accuracy is done using signal-to- noise (S/N) ratios and OA. OA has been noted for limitations in handling a large number of variables. Therefore, in this research, we propose the use of a random binary search (RBS) algorithm incorporated within MTS for optimizing the procedure for selecting the most useful variables. Ensemble is a powerful technique to achieve improvement in the accuracy of predictive models, whereby individual methods, which are not consistently the best performers in different problems and datasets, are brought together to provide predictions which are more accurate than those made by individual methods

A Significant Feature Selection in the Mahalanobis Taguchi System using Modified-Bees Algorithm

This paper compares the performance of orthogonal array (OA), modified-Bees Algorithm (mBA) and conventional Bees Algorithm (BA) in significant feature selection scheme (optimization) of the Mahalanobis-Taguchi System (MTS) methodology. The main contribution of this work is to address both performances in terms of computing cost i.e. computing time as well as classification accuracy rate. Several studies have been conducted to evaluate the performance of OA against other heuristic search techniques in MTS methodology however, discussions in terms of the computing speed performances were found to be lacking. Instead, the accuracy performances were given the emphasis by drawing criticisms towards the deployment of OA as ineffective as compared to other state-of-the-art heuristic algorithms. Bees Algorithm (BA) is one heuristic search technique that discovers optimal (or near optimal) solutions using search strategy mimics the social behaviour of a honeybee colony. In this comparison work, modified-BA (mBA) is introduced into the optimization scheme of MTS with a modification on its neighbourhood search mechanism from the original BA. Instead of searching in random mode, a backward selection method is proposed. MD is used as the result assessment metric while the larger-the-better type of SNR is deployed as the algorithm\u27s objective function. The historical heart liver disease data are used as the case study on which the comparisons between OA, mBA and BA performances specifically in terms of the computing speed are made and addressed. The outcomes showed a promising performance of the mBA as compared to OA with a comparable classification accuracy rate. Eventhough OA outperformed mBA in terms of computational speed, the MTS manage to classify at the expense of lower number of variables suggested by mBA. The mBA also converges faster than the conventional BA in finding the potential solution of the case problem

Binary particle swarm optimization for variables selection optimization Taguchi's T method

Prediction analysis has drawn signi?cant interest in numerous ?elds Taguchi’s T-Method is a prediction tool that was practically developed to predict even with a limited sample data. It was developed explicitly for multivariate system prediction by relying on historical data as the baseline model and adapting the signal to noise ratio (SNR) as well as zero proportional concepts in strengthening its robustness. Orthogonal array (OA) in T-Method is a variable selection optimization technique in improving the prediction accuracy as well as helping to eliminate variables that may deteriorate the overall performance. However, the limitation of OA in dealing with higher multivariate data restrains the optimization accuracy. The binary particle swarm optimization used in this study helps to cater to the limitation of OA as well as optimizing the variable selection process for better prediction accuracy. A comparison between the T-Method+OA and T- Method+BPSO in four di?erent case studies shows that the T-Method+BPSO performs better with a higher coe?cient of determination (R2) value and means relative error (MRE) value compared to the T-Method+OA. The T-Method with the BPSO element as variables screening optimization is able to increase or even maintain the prediction accuracy for cases that are normally distributed, have a high R2value, and with low sample data

Optimizing the MFlex monitoring system using Mahalanobis-Taguchi system

Methadone Flexi Dispensing Service (MFlex) has been officially re-branded from Methadone 1Malaysia Service (M1M) since 2nd January 2019. Patients under MFlex are frequently taking their methadone according to a plan provided by pharmacist at public clinic. From the dose monitoring taken annually, pharmacists can predict critical patients based on high monthly dose increases. However, the current monitoring system is written documentation with total doses that cannot accurately measure addiction levels and slow down the distribution process to appropriate incentives as provided by the government. The main objective of this work is to develop a new data monitoring system by evaluating all factors contributed to the addiction level. Mahalanobis-Taguchi System (MTS) is a method of predicting and diagnosing system performance using multivariate data in order to make quantitative decisions with the construction of a multivariate measurement scale using an analytical method. The results show that the minimum Mahalanobis Distance (MD) for healthy data is 0.2245 while the maximum is 2.3380. The minimum and maximum MD of unhealthy data is 0.6077 and 24.5719 respectively. Thus, parameters of blood, bilirubin, nitrite, specific gravity, leukocytes are considered as significant parameters by considering positive value signal-to-noise ratio (SNR) gain. Graphical user interface (GUI) has been developed for analyzing the normal and abnormal patients in detail. Meanwhile, mobile application has been developed as a decision-making tool to classify that the patients is either normal or abnormal