Support Vector Machines Approach to Pattern Detection in Bankruptcy Prediction and Its Contingency

2004 ◽  
pp. 1254-1259 ◽  
Author(s):  
Kyung-shik Shin ◽  
Kyoung Jun Lee ◽  
Hyun-jung Kim
Keyword(s):  
Support Vector Machines ◽  
Bankruptcy Prediction ◽  
Pattern Detection ◽  
Support Vector ◽  
Vector Machines

scholarly journals Bankruptcy Prediction of Engineering Companies in the EU Using Classification Methods

2018 ◽  
Vol 66 (5) ◽  
pp. 1347-1356 ◽  
Author(s):  
Michaela Staňková ◽  
David Hampel
Keyword(s):  
Logistic Regression ◽  
Support Vector Machines ◽  
Binary Classification ◽  
Classification Tree ◽  
Classification Trees ◽  
Bankruptcy Prediction ◽  
Support Vector ◽  
Type I ◽  
Vector Machines ◽  
The Eu

This article focuses on the problem of binary classification of 902 small- and medium‑sized engineering companies active in the EU, together with additional 51 companies which went bankrupt in 2014. For classification purposes, the basic statistical method of logistic regression has been selected, together with a representative of machine learning (support vector machines and classification trees method) to construct models for bankruptcy prediction. Different settings have been tested for each method. Furthermore, the models were estimated based on complete data and also using identified artificial factors. To evaluate the quality of prediction we observe not only the total accuracy with the type I and II errors but also the area under ROC curve criterion. The results clearly show that increasing distance to bankruptcy decreases the predictive ability of all models. The classification tree method leads us to rather simple models. The best classification results were achieved through logistic regression based on artificial factors. Moreover, this procedure provides good and stable results regardless of other settings. Artificial factors also seem to be a suitable variable for support vector machines models, but classification trees achieved better results using original data.

An application of support vector machines in bankruptcy prediction model

2005 ◽  
Vol 28 (1) ◽  
pp. 127-135 ◽  
Author(s):  
Kyung-Shik Shin ◽  
Taik Soo Lee ◽  
Hyun-jung Kim
Keyword(s):  
Support Vector Machines ◽  
Prediction Model ◽  
Bankruptcy Prediction ◽  
Support Vector ◽  
Vector Machines

Support vector machines for anti-pattern detection

2012 ◽  
Author(s):  
Abdou Maiga ◽  
Nasir Ali ◽  
Neelesh Bhattacharya ◽  
Aminata Sabané ◽  
Yann-Gaël Guéhéneuc ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Pattern Detection ◽  
Support Vector ◽  
Vector Machines

scholarly journals RECENT ADVANCES ON SUPPORT VECTOR MACHINES RESEARCH

2012 ◽  
Vol 18 (1) ◽  
pp. 5-33 ◽  
Author(s):  
Yingjie Tian ◽  
Yong Shi ◽  
Xiaohui Liu
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Bankruptcy Prediction ◽  
Optimization Models ◽  
Support Vector ◽  
Problem Solution ◽  
Vector Machines ◽  
Credit Risk Analysis

Support vector machines (SVMs), with their roots in Statistical Learning Theory (SLT) and optimization methods, have become powerful tools for problem solution in machine learning. SVMs reduce most machine learning problems to optimization problems and optimization lies at the heart of SVMs. Lots of SVM algorithms involve solving not only convex problems, such as linear programming, quadratic programming, second order cone programming, semi-definite programming, but also non-convex and more general optimization problems, such as integer programming, semi-infinite programming, bi-level programming and so on. The purpose of this paper is to understand SVM from the optimization point of view, review several representative optimization models in SVMs, their applications in economics, in order to promote the research interests in both optimization-based SVMs theory and economics applications. This paper starts with summarizing and explaining the nature of SVMs. It then proceeds to discuss optimization models for SVM following three major themes. First, least squares SVM, twin SVM, AUC Maximizing SVM, and fuzzy SVM are discussed for standard problems. Second, support vector ordinal machine, semisupervised SVM, Universum SVM, robust SVM, knowledge based SVM and multi-instance SVM are then presented for nonstandard problems. Third, we explore other important issues such as lp-norm SVM for feature selection, LOOSVM based on minimizing LOO error bound, probabilistic outputs for SVM, and rule extraction from SVM. At last, several applications of SVMs to financial forecasting, bankruptcy prediction, credit risk analysis are introduced.

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