Abstract
SME Credit rating index system becomes a significant research topic in recent years. So many researches have focused on this topic. However, the existing researches are only focused on one aspect of the SME Credit Rating problem. In order to resolve this problem, in this paper, we use the idea of ensemble learning, which integrated several basic machine learning algorithms to improve the learning result. Through further amendments, we build a set of SME corporate credit evaluation models which have higher forecast accuracy and stronger anti-jamming capability. Finally, we prove the effectiveness of our model through carrying out a set of experiments.
Highlights
SME, as an emerging research area, has attracted so many researchers in recent years [1] [2]
The rest of the paper is organized as follow: in Section 2, we briefly review some related works of SME credit rating
Foreign credit rating can be roughly divided into three stages: Expert judgment stage, Credit scoring stage and Comprehensive evaluation stage
Summary
SME, as an emerging research area, has attracted so many researchers in recent years [1] [2]. The most fundamental way to solve the above problems is to evaluate SME credit conditions. It will reduce the risk of financial intermediaries and the corresponding transaction costs. In this paper, based on the previous works, we build a set of targeted credit evaluation models according to the characteristics of SME. How to cite this paper: Wang, L.M., et al (2014) Integrated Learning-Based SME Credit Rating. The rest of the paper is organized as follow: in Section 2, we briefly review some related works of SME credit rating.
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