| Summary: | 碩士 === 國立交通大學 === 工業工程與管理學系 === 99 === Most of the finance risk data with a class imbalance problem. Class imbalanced data means the asymmetric categories of data, a data with class imbalance problem could be divided into two categories: major class data and minor class data. If we use all the imbalanced data without sampling, the accuracy of major class instances could be very well, but poor predictive ability to identify minority instances. Many risk assessment models have been developed in many studies, but most of them use sampling method to deal with the class imbalanced data. This study use “Granular Computing” model to tackle class imbalance problems. Using Granular computing to construct risk model could provide a better insight into the essence of data, and effectively solve class imbalance problems. In order to improve the lack of Granular Computing, and enhance the efficiency of credit risk modeling, this study adds a new index: “PM” to avoid a situation which minor class data spread to major class granular. In the end, the study compares the granular computing risk assessment model with several sampling methods. By calculation and compare of the accuracy, AUC and G-means, we can conclude that using granular computing credit assessment model would have same or even better result than sampling models.
|
|---|
