Feature Selection in Big Data by Using the enhancement of Mahalanobis–Taguchi System; Case Study, Identifiying Bad Credit clients of a Private Bank of Islamic Republic of Iran

The Mahalanobis-Taguchi System (MTS) is a relatively new collection of methods proposed for diagnosis and forecasting using multivariate data. It consists of two main parts: Part 1, the selection of useful variables in order to reduce the complexity of multi-dimensional systems and part 2, diagnosis and prediction, which are used to predict the abnormal group according to the remaining useful variables. The main purpose of this research is presenting a new method to select useful variables by using and combining the concept of Mahalanobis distance and Integer Programming. Due to the inaccuracy and the difficulties in selecting the useful variables by the design of experiments method, we have used an innovative and accurate method to solve the problem. The proposed model finds the solutions faster and has a better performance than other common methods.

Keywords:

Multivariate Analysis System , Mahalanobis-Taguchi System , Mixed Integer Programming , Machine Learning