Determination of optimal unit space data for taguchi’s t-method based on homogeneity of output

Taguchi’s T-method is a prediction model introduced by Genichi Taguchi under the Mahalanobis- Taguchi System to determine the future state or unknown output based on existing or historical data. The prediction model was constructed using normalized signal data involving subtraction of average value of unit space data from signal data. The objective of this research is to determine a group of data having homogeneous characteristics from a densely populated region in a dataset to functioned as a basis for unit space data selection in T-method for predicting an accurate outcome. Histogram was utilized as a tool in representing data in multiple groups and a group with highest data frequency defined as unit space data. Nine different number of bins was used in assessing the effect of unit space data towards prediction accuracy. The result from the experiments on six different datasets indicates that no single number of bin fit for all in offering an optimal result. In addition, the size of unit space data and signal data do not significantly affect the final outcome. However, except for Auto MPG dataset, all different numbers of bin resulted in better prediction accuracy with less MSE and RMSE as compared to conventional T-method

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

An overview of Taguchi’ s T-Method as a prediction tool for multivariate analysis

Analysis of prediction has attracted considerable interest in various fields. Taguchi’s T-Method is a prediction method introduced by Genichi Taguchi in mid-year 2000, among several other Mahalanobis Taguchi system tools. It was explicitly created for the prediction of multivariate data. Taguchi's T-Method has shown that even with limited sample size, making a prediction based on historical data is possible. The key elements that have been adapted in reinforcing Taguchi’s TMethod robustness are by introducing the unit-space principle and adaptation of the signal to the noise ratio (SNR) as a weighting as well as a zero-proportional theory, as proposed by Genichi Taguchi in a robust model. Taguchi’s T-Method was widely practicing in Japan and began to be practiced by non-Japanese researchers due to its simplicity and simple understanding. Up to recent, various applications of Taguchi’s T-Method been applied, which prove to be beneficial to industrial needs. This research paper outlines the T-method procedures by applying it in a few benchmark datasets and compare the accuracy with the existing multiple linear regression method for an overview. The results show that Taguchi’s T-Method is better than multiple regression in dealing with limited sample data in which the sample size is smaller than the input variables. Taguchi’s TMethod proved to have the ability to predict output with an acceptable range of prediction accuracy

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

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

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

Binary Bitwise Artificial Bee Colony as Feature Selection Optimization Approach within Taguchi's T-Method

Taguchi's T-Method is one of the Mahalanobis Taguchi System-(MTS-) ruled prediction techniques that has been established specifically but not limited to small, multivariate sample data. The prediction model's complexity aspect can be further enhanced by removing features that do not provide valuable information on the overall prediction. In order to accomplish this, a matrix called orthogonal array (OA) is used within the existing Taguchi's T-Method. However, OA's fixed-scheme matrix and its drawback in coping with the high-dimensionality factor led to a suboptimal solution. On the contrary, the usage of SNR (dB) as its objective function was a reliable measure. The application of Binary Bitwise Artificial Bee Colony (BitABC) has been adopted as the novel search engine that helps cater to OA's limitation within Taguchi's T-Method. The generalization aspect using bootstrap was a fundamental addition incorporated in this research to control the effect of overfitting in the analysis. The adoption of BitABC has been tested on eight (8) case studies, including large and small sample datasets. The result shows improved predictive accuracy ranging between 13.99% and 32.86% depending on cases. This study proved that incorporating BitABC techniques into Taguchi's T-Method methodology effectively improved its prediction accuracy

Binary bitwise artificial bee colony as feature selection optimization approach within Taguchi’s T-method

Taguchi’s T-Method is one of the Mahalanobis Taguchi System- (MTS-) ruled prediction techniques that has been established specifically but not limited to small, multivariate sample data. The prediction model’s complexity aspect can be further enhanced by removing features that do not provide valuable information on the overall prediction. In order to accomplish this, a matrix called orthogonal array (OA) is used within the existing Taguchi’s T-Method. However, OA’s fixed-scheme matrix and its drawback in coping with the high-dimensionality factor led to a suboptimal solution. On the contrary, the usage of SNR (dB) as its objective function was a reliable measure. The application of Binary Bitwise Artificial Bee Colony (BitABC) has been adopted as the novel search engine that helps cater to OA’s limitation within Taguchi’s T-Method. The generalization aspect using bootstrap was a fundamental addition incorporated in this research to control the effect of overfitting in the analysis. The adoption of BitABC has been tested on eight (8) case studies, including large and small sample datasets. The result shows improved predictive accuracy ranging between 13.99% and 32.86% depending on cases. This study proved that incorporating BitABC techniques into Taguchi’s T-Method methodology effectively improved its prediction accuracy

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