Artificial Bee Colony for Features Selection Optimization in Increasing T-Method Accuracy

Harudin, Nolia; Jamaludin, Khairur Rijal; Muhtazaruddin, Mohd Nabil; Ramlie, Faizir; Muhammad, Wan Zuki Azman Wan; Jaafar, NN

doi:10.14419/ijet.v7i4.35.26276

Cited by 4 publications

(7 citation statements)

References 15 publications

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“…In [30], the authors applied a stepwise forward and backward selection procedure for this purpose which showed an increase in accuracy in many cases conducted [30]. e author of [31] suggested a Binary Artificial Bee Colony (BABC) algorithm, and the findings revealed that T-Method + BABC worked better than T-Method + OA in a particular case study conducted [31]. e most recent reported study by [32] has specifically addressed OA's downside and suggested Binary Particle Swarm Optimization (BPSO), which indicates an increase in accuracy for specific case studies [32].…”

Section: Taguchi's T-methods For the Feature Selection Optimizationmentioning

confidence: 99%

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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

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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.

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Section: Taguchi's T-methods For the Feature Selection Optimizationmentioning

confidence: 99%

“…e previous research by [31,32] was further expanded in this study by proposing the other variant of binary ABC called Binary Bitwise ABC algorithms with proper generalization aspect been amended into it, which is the application of bootstrap cross-validation.…”

Section: Taguchi's T-methods For the Feature Selection Optimizationmentioning

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

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“…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. The findings were concluded to be case-to-case dependent.…”

Section: Related Studiesmentioning

confidence: 99%

“…Up until recently, the element of OA in the MT-Method has been continuously enhanced with various machine-learning algorithm approaches. Unlike the MT-Method, enhancement of the OA element within the T-Method for variables optimization is still at an initial stage but Harudin et al [10] did enhance the OA in the T-Method with an artificial bee colony algorithm (ABC) and the results showed that T-Method+ABC performed better compared to T-Method+OA for the specific case study conducted. There are several other studies done with the same intention which are yet to be published but have been shared in several forums.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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“…The BBA belongs to the swarm intelligence-based along with particle swarm optimization (PSO), ant colony optimization, honey bee swarm optimization algorithm, cuckoo search optimization, and many others. Specifically, in optimizing the Tmethod prediction accuracy, Harudin et al [13] successfully employed the artificial bee colony algorithm for feature selection optimization. The result shows an improvement in prediction accuracy as compared to the conventional OA approach.…”

Section: Introductionmentioning

confidence: 99%

Taguchi's T-method with nearest integer-based binary bat algorithm for prediction

Marlan

Jamaludin²,

Ramlie³

et al. 2022

Bulletin EEI

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Taguchi’s T-method is a new prediction technique under the Mahalanobis-Taguchi system to predict unknown output or future states based on available historical information. Conventionally, in optimizing the T-method prediction accuracy, Taguchi’s orthogonal array is utilized to determine a subset of significant features to be used in formulating the optimal prediction model. This, however, resulted in a sub-optimal prediction accuracy due to its fixed and limited feature combination offered for evaluation and lack of higher-order feature interaction. In this paper, a swarm-based binary bat optimization algorithm with a nearest integer discretization approach is integrated with the Taguchi’s T-method. A comparative study is conducted by comparing the performance of the proposed method against the conventional approach using mean absolute error as the performance measure on four benchmark case studies. The results from experimental studies show a significant improvement in the T-method prediction accuracy. A reduction in the total number of features results in a less complex model. Based on the general observation, the nearest integer-based binary bat algorithm successfully optimized the selection of significant features due to recursive and repetitive searchability, in addition to its adaptive element in response to the current best solution in guiding the search process towards optimality.

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Artificial Bee Colony for Features Selection Optimization in Increasing T-Method Accuracy

Cited by 4 publications

References 15 publications

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

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

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

Taguchi's T-method with nearest integer-based binary bat algorithm for prediction

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