| Summary: | 博士 === 國立交通大學 === 工業工程與管理系所 === 92 === Taguchi Methods have been successfully applied to many engineering application to improve the performance of product and process. The Mahalanobis-Taguchi System (MTS) developed by Dr. Taguchi is a relatively new technology for diagnosis and forecasting using multivariate data. MTS is based on the principles of quality engineering and Mahalanobis distance (MD). This technology is aimed at providing a better prediction for multivariate data through the construction of a multivariate measurement scale. The results of MTS can be a reference for making decisions.
For development and applications of MTS, to understand the features is very important. In this dissertation, first, we study the features of MTS, and evaluate the performance of MTS. Through changing the conditions of experiments including Mahalanobis Space, Sampling and Signal to Noise ratios, the performance of MTS was evaluated. Iris data was used to illustrate the performance of MTS. Secondly, the credit scoring system based on MTS is proposed. Credit scoring is widely used to make credit decisions, to reduce the cost of credit analysis and to make decisions fast. There are many credit scoring techniques, however, traditional credit scoring models do not consider the influence of noises. The effectiveness of the proposed MTS approach is demonstrated by real case data from a large Taiwanese bank.
In addition, we propose a modified approach that is combined logistic regression (LR) with Taguchi’s approach for improving the shortcoming of categorical data of MTS. Results are benchmarked against two traditional methods including decision tree and linear discriminant analysis. Due to the results of the above, both MTS and modified approach (LRTM) can be applied to credit scoring system.
Finally, several important issues regarding to the MTS and the suggestions of future research are summarized in the conclusion.
Key words: Mahalanobis-Taguchi System (MTS), Mahalanobis distance, orthogonal array, Signal-to-Noise ratio, Logistic regression, Credit scoring
|
|---|
