Using Mahalanobis–Taguchi system, logistic regression, and neural network method to evaluate purchasing audit quality

Abstract

The feature selection function in data mining facilitates the classification of vast data volumes and reduces attribute variables, enabling the construction of classification prediction models. For binary data, the Mahalanobis-Taguchi system, the logistic regression method, and the neural network method all feature high stability and accuracy. The MahalanobisTaguchi system differs from the other two methods in that models are developed through a measurement scale rather than from the learning of analytical data. We analyzed the audit quality of Taipei City Government public procurement from supply chains (hereafter abbreviated as public procurement) and applied the three feature selection methods to determine the items used in public procurement audit quality questionnaires. The results showed that the predictive powers of Mahalanobis-Taguchi system and logistic regression methods for the reduced question items were 93.8% and 92.5%, respectively. Furthermore, the prediction accuracy rate of the neural network method was 100%, showing that the prediction model constructed using the feature selections to reduce the number of attributes can effectively lower the number of questionnaire items while maintaining high prediction accuracy.

Modelling the Dynamic Impacts of High Speed Rail Operation on Regional Public Transport—From the Perspective of Energy Economy

“…However, it is difficult to conduct a systematic analysis of the regional transport market to uncover the impact of the THSR simply by examining changes in the market share of different modes [5,28]. Therefore, it is necessary to apply our model to better understand the dynamic rivalry between THSR and the conventional rail and air transport in Taiwan from a regional market point of view [29][30][31][32][33][34]. From the analysis based on the model, we can reveal the trend of influence between transport modes, and these findings can be utilized as forecasting references [35][36][37][38][39].…”

Nonlinear Effect of Financial Efficiency and Financial Competition on Heterogeneous Firm R&D: A Study on the Combined Perspective of Financial Quantity Expansion and Quality Development

“…In this section, we study the impact of qualitative improvements in financial development on firms' R&D and further discuss the impact of financial development on the allocation efficiency of firms' R&D [37][38][39][40][41][42][43]. Therefore, we use firm R&D as the explained variable, the qualitative indicators of financial development (financial efficiency, financial competition) as the threshold variables of financial scale development, and enterprise heterogeneity (total factor productivity) and the other variables as explanatory variables [44][45][46][47][48][49][50].…”

Applying the Mahalanobis–Taguchi System to Improve Tablet PC Production Processes

“…The Mahalanobis distance of a product from an abnormal sample that is calculated using the means, standard deviations, and correlation coefficient inverse matrices of base space is extremely high. Additionally, thresholds can be determined using the smallest type-I error (normal products misjudged as abnormal products) and type-II errors (abnormal products misjudged as normal products) that occur [15,16].…”

“…Nevertheless, the MTS method features high stability and an accuracy rate higher than 98%. Thus, the MTS, which involves using data mining and classification methods to reduce attribute variables for building prediction models, demonstrates high discrimination ability in practice [16]. The type-I errors (i.e., the analysis methods showed that tablet PCs were abnormal products when in fact they were normal products), and type-II errors (i.e., the tablets PCs were abnormal products that failing to judge as normal products) were identified for each method.…”