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.

中文翻译：

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Taguchi T方法中的二进制按位人工蜂群作为特征选择优化方法

Taguchi的T方法是Mahalanobis Taguchi系统（MTS-）统治的预测技术之一，已专门建立但不限于小的多元变量数据。预测模型的复杂性方面可以通过删除不提供有关整体预测的有价值信息的功能来进一步增强。为了做到这一点，在现有的田口T方法中使用了称为正交阵列（OA）的矩阵。但是，OA的固定方案矩阵及其在应对高维因子方面的缺点导致了次优的解决方案。相反，使用SNR（dB）作为其目标函数是一种可靠的方法。二进制按位人工蜂群（BitABC）的应用已被用作新颖的搜索引擎，有助于满足田口T方法中OA的局限性。使用引导程序的泛化方面是此研究中引入的一个基本附加功能，用于控制分析中过拟合的影响。已在八（8）个案例研究中测试了BitABC的采用，包括大型和小型样本数据集。结果表明，根据情况，预测准确性有所提高，范围在13.99％和32.86％之间。这项研究证明，将BitABC技术整合到Taguchi的T-Method方法中可以有效地提高其预测准确性。