원문정보
초록
영어
With the rapid growth of internet finance, the credit assessing is becoming more and more important. An effective classification model will help financial institutions gain more profits and reduce the loss of bad debts. In this paper, we propose a new Support Vector Machine (SVM) based ensemble model (SVM-BRS) to address the issue of credit analysis. The model combines random subspace strategy and boosting strategy, which encourages diversity. SVM is considered as a state-of-art model to solve classification problem. Therefore, the proposed model has the potential to generate more accuracy classification. Accordingly, this study compares the ANN, LR, SVM, Bagging SVM, Boosting SVM techniques and experience shows that the new SVM based ensemble model can be used as an alternative method for credit assessing.
목차
1. Introduction
2. Background
2.1 Bagging
2.2 Random Subspace
2.3 Boosting
2.4 Support Vector Machine
3. A New SVM based Ensemble Model for Credit Analysis
3.1. Partitioning Original Data
3.2. Creating diversity support vector machine
3.3. Creating Boosting SVM
3.4. Integrating Diversity Classifiers into an Ensemble Output
4. Experimental Analysis
4.1. Data Set
4.2. Evaluation Criteria
4.3. Experimental Results
5. Conclusions
References