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.

Predicting corporate credit rating based on qualitative information of MD&A transformed using document vectorization techniques

2020 ◽  
Vol 54 (2) ◽  
pp. 151-168
Author(s):  
Jinwook Choi ◽  
Yongmoo Suh ◽  
Namchul Jung
Keyword(s):  
Credit Rating ◽  
Prediction Models ◽  
Quantitative Information ◽  
Qualitative Information ◽  
Rating Data ◽  
Content Type ◽  
Financial Variables ◽  
Corporate Credit ◽  
Rating Prediction ◽  
Corporate Credit Rating

PurposeThe purpose of this study is to investigate the effectiveness of qualitative information extracted from firm’s annual report in predicting corporate credit rating. Qualitative information represented by published reports or management interview has been known as an important source in addition to quantitative information represented by financial values in assigning corporate credit rating in practice. Nevertheless, prior studies have room for further research in that they rarely employed qualitative information in developing prediction model of corporate credit rating.Design/methodology/approachThis study adopted three document vectorization methods, Bag-Of-Words (BOW), Word to Vector (Word2Vec) and Document to Vector (Doc2Vec), to transform an unstructured textual data into a numeric vector, so that Machine Learning (ML) algorithms accept it as an input. For the experiments, we used the corpus of Management’s Discussion and Analysis (MD&A) section in 10-K financial reports as well as financial variables and corporate credit rating data.FindingsExperimental results from a series of multi-class classification experiments show the predictive models trained by both financial variables and vectors extracted from MD&A data outperform the benchmark models trained only by traditional financial variables.Originality/valueThis study proposed a new approach for corporate credit rating prediction by using qualitative information extracted from MD&A documents as an input to ML-based prediction models. Also, this research adopted and compared three textual vectorization methods in the domain of corporate credit rating prediction and showed that BOW mostly outperformed Word2Vec and Doc2Vec.

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