A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach

2012 ◽  
Vol 39 (8) ◽  
pp. 1800-1811 ◽  
Author(s):  
Kyoung-jae Kim ◽  
Hyunchul Ahn
Keyword(s):  
Support Vector Machines ◽  
Credit Rating ◽  
Support Vector ◽  
Corporate Credit ◽  
Vector Machines ◽  
Corporate Credit Rating ◽  
Rating Model

Identification of best discrimination surface by mixed-integer semi-definite programming for support vector machine

2021 ◽  
Author(s):  
Katsuhiro Tanaka ◽  
Rei Yamamoto
Keyword(s):  
Support Vector Machine ◽  
Credit Rating ◽  
Positive Definite ◽  
Mixed Integer ◽  
Support Vector ◽  
Discrimination Accuracy ◽  
Rating Data ◽  
Corporate Credit ◽  
Quadratic Surface ◽  
Corporate Credit Rating

This paper proposes two improvements to the support vector machine (SVM): (i) extension to a semi-positive definite quadratic surface, which improves the discrimination accuracy; (ii) addition of a variable selection constraint. However, this model is formulated as a mixed-integer semi-definite programming (MISDP) problem, and it cannot be solved easily. Therefore, we propose a heuristic algorithm for solving the MISDP problem efficiently and show its effectiveness by using corporate credit rating data.

scholarly journals A Corporate Credit Rating Model Using Support Vector Domain Combined with Fuzzy Clustering Algorithm

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Xuesong Guo ◽  
Zhengwei Zhu ◽  
Jia Shi
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Credit Rating ◽  
Computational Cost ◽  
Support Vector ◽  
Artificial Intelligence Techniques ◽  
Corporate Credit ◽  
Fuzzy Clustering Algorithm ◽  
Corporate Credit Rating

Corporate credit-rating prediction using statistical and artificial intelligence techniques has received considerable attentions in the literature. Different from the thoughts of various techniques for adopting support vector machines as binary classifiers originally, a new method, based on support vector domain combined with fuzzy clustering algorithm for multiclassification, is proposed in the paper to accomplish corporate credit rating. By data preprocessing using fuzzy clustering algorithm, only the boundary data points are selected as training samples to accomplish support vector domain specification to reduce computational cost and also achieve better performance. To validate the proposed methodology, real-world cases are used for experiments, with results compared with conventional multiclassification support vector machine approaches and other artificial intelligence techniques. The results show that the proposed model improves the performance of corporate credit-rating with less computational consumption.

Credit rating analysis with support vector machines and neural networks: a market comparative study

2004 ◽  
Vol 37 (4) ◽  
pp. 543-558 ◽  
Author(s):  
Zan Huang ◽  
Hsinchun Chen ◽  
Chia-Jung Hsu ◽  
Wun-Hwa Chen ◽  
Soushan Wu
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Comparative Study ◽  
Credit Rating ◽  
Support Vector ◽  
Vector Machines
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