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.

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.

A fuzzy clustering algorithm based on hybrid surrogate model

2021 ◽  
pp. 1-14
Author(s):  
Maolin Shi ◽  
Zihao Wang ◽  
Lizhang Xu
Keyword(s):  
Fuzzy Clustering ◽  
Surrogate Model ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Surrogate Models ◽  
Support Vector ◽  
Fuzzy Clustering Algorithm ◽  
Regression Relationship ◽  
Synthetic Datasets

Data clustering based on regression relationship is able to improve the validity and reliability of the engineering data mining results. Surrogate models are widely used to evaluate the regression relationship in the process of data clustering, but there is no single surrogate model that always performs the best for all the regression relationships. To solve this issue, a fuzzy clustering algorithm based on hybrid surrogate model is proposed in this work. The proposed algorithm is based on the framework of fuzzy c-means algorithm, in which the differences between the clusters are evaluated by the regression relationship instead of Euclidean distance. Several surrogate models are simultaneously utilized to evaluate the regression relationship through a weighting scheme. The clustering objective function is designed based on the prediction errors of multiple surrogate models, and an alternating optimization method is proposed to minimize it to obtain the memberships of data and the weights of surrogate models. The synthetic datasets are used to test single surrogate model-based fuzzy clustering algorithms to choose the surrogate models used in the proposed algorithm. It is found that support vector regression-based and response surface-based fuzzy clustering algorithms show competitive clustering performance, so support vector regression and response surface are used to construct the hybrid surrogate model in the proposed algorithm. The experimental results of synthetic datasets and engineering datasets show that the proposed algorithm can provide more competitive clustering performance compared with single surrogate model-based fuzzy clustering algorithms for the datasets with regression relationships.

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