A feasibility study in adapting Shamos Bickel and Hodges Lehman estimator into T-Method for normalization

Harudin, Nolia; Jamaludin, Khairur Rijal; Muhtazaruddin, Mohd Nabil; Ramlie, Faizir; Muhamad, Wan Zuki Azman Wan

doi:10.1088/1757-899x/319/1/012033

Cited by 6 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It appears that the T-Method is more reliable for data with a lower sample size, as shown by the case studies shared, except for the estimation of building energy performance case study. Similar findings have also been reported by [4], [23][24], [27][28]. Cases that deal with high sample data might be at risk since linear regression analysis tends to be biased towards nonlinear and outliers that lead to non-normality issues.…”

Section: Resultssupporting

confidence: 85%

“…Since the T-Method has been progressively explored up until recently, most of the cases use the T-Methodin real life prediction problems [22][23][24]. Inoh [25] improved the method to define the unit space theory into T a and T b methods while [3], [4], [26][27][28][29] focused on improving the baseline model accuracy for outliers and abnormal case data. Harudin et al [10] is the only published work outside Japan currently that improved the variables selection optimization in the T-Method using ABC.…”

Section: Related Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Binary Particle Swarm Optimization for Variables Selection Optimization in Taguchi’s T-Method

et al. 2020

View full text Add to dashboard Cite

Prediction analysis has drawn significant interest in numerous field. Taguchi’s T-Method is a prediction tool that developed practically but not limited to small sample analysis. It was developed explicitly for multidimensional 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 help in eliminating variables that may deteriorate the overall performance. However, the limitation of OA in dealing with higher multidimensionality restraint the optimization accuracy. Binary particle swarm optimization used in this study helps to cater to the limitation of OA as well as optimizing the variable selection process to better prediction accuracy. The results show that if the historical data consist of samples with higher correlation of determination (R2) value for the model creation, the optimization process in reducing the number of variables would be much reliable and accurate. Comparing between T-Method+OA and T-Method+BPSO in four different case study, it shows that T-Method+BPSO performing better with greater R2 and means relative error (MRE) value compared to T-Method+OA.

show abstract

Section: Resultssupporting

confidence: 85%

Section: Related Studiesmentioning

confidence: 99%

Binary Particle Swarm Optimization for Variables Selection Optimization in Taguchi’s T-Method

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Additionally, Taguchi's T-Method has no direct influence from multicollinearity since individual regression has been considered [2,15,16]. Based on the number of papers published in the literature, Taguchi's T-Method studies' progress is moving towards optimizing parameters and optimizing feature selection rather than just application purposes since the year 2012 [17][18][19][20]. e increasing pattern has indirectly triggered that there are indeed a variety of enhanced approaches towards parameter and feature selection optimization available out there that can be further explored and incorporated into Taguchi's T-Method as a hybridization or integration element.…”

Section: Introductionmentioning

confidence: 99%

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

Harudin

Ramlie

Muhamad

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

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.

show abstract

Taguchi’s T-method with Normalization-Based Binary Bat Algorithm

Marlan,

Jamaludin,

Harudin

2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

A feasibility study in adapting Shamos Bickel and Hodges Lehman estimator into T-Method for normalization

Cited by 6 publications

References 12 publications

Binary Particle Swarm Optimization for Variables Selection Optimization in Taguchi’s T-Method

Binary Particle Swarm Optimization for Variables Selection Optimization in Taguchi’s T-Method

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

Taguchi’s T-method with Normalization-Based Binary Bat Algorithm

Contact Info

Product

Resources

About