Hybrid genetic algorithms and support vector machines for bankruptcy prediction

Min, Sung-Hwan; Lee, Jumin; Han, Ingoo

doi:10.1016/j.eswa.2005.09.070

Cited by 336 publications

(160 citation statements)

References 23 publications

Supporting

Mentioning

153

Contrasting

Unclassified

Order By: Relevance

“…Min et al [64] Stepwise search with criteria optimized for discriminant analysis and logistic regression, and t test applied to variables used in previous studies ?…”

Section: Mlp-bp -Som-mlpmentioning

confidence: 99%

Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy

Jardin

2010

Neurocomputing

110

View full text Add to dashboard Cite

We evaluate the prediction accuracy of models designed using different classification methods depending on the technique used to select variables, and we study the relationship between the structure of the models and their ability to correctly predict financial failure. We show that a neural network based model using a set of variables selected with a criterion that it is adapted to the network leads to better results than a set chosen with criteria used in the financial literature. We also show that the way in which a set of variables may represent the financial profiles of healthy companies plays a role in Type I error reduction.

show abstract

“…Min et al [64] Stepwise search with criteria optimized for discriminant analysis and logistic regression, and t test applied to variables used in previous studies ?…”

Section: Mlp-bp -Som-mlpmentioning

confidence: 99%

Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy

Jardin

2010

Neurocomputing

110

View full text Add to dashboard Cite

show abstract

“…GAs have been the optimizers of choice in various artificial intelligence applications, exhibiting better performance than other non-linear optimization approaches to parameter tuning. Such applications include parameter tuning in support vector machines [38], [39], tuning of neural network weights for on-line training [40], tuning of range image segmentation algorithms [41], and the fine-tuning of a parametric active contour model which is supplementary to the Taguchi approach [32].…”

Section: Genetic Optimization Frameworkmentioning

confidence: 99%

A genetically optimized level set approach to segmentation of thyroid ultrasound images

et al. 2007

View full text Add to dashboard Cite

Abstract. This paper presents a novel framework for thyroid ultrasound image segmentation that aims to accurately delineate thyroid nodules. This framework, named GA-VBAC incorporates a level set approach namedVariable Background Active Contour model (VBAC) that utilizes variable background regions, to reduce the effects of the intensity inhomogeneity in the thyroid ultrasound images. Moreover, a parameter tuning mechanism based on Genetic Algorithms (GA) has been considered to search for the optimal VBAC parameters automatically, without requiring technical skills. Experiments were conducted over a range of ultrasound images displaying thyroid nodules. The results show that the proposed GA-VBAC framework provides an efficient, effective and highly objective system for the delineation of thyroid nodules.

show abstract

“…The use of single model would cause the overvalued or undervalued predictive ability of model due to the each kind of method with more or less fixed defects. For that reason, Sung-Hwan Min, Jumin Lee et al (2006) combined the genetic algorithm and SVW to predict bankruptcy, improved the performance of SVW in two aspects of feature subset selection and parameters optimization. In addition, there were other scholars such as Zhongsheng Hua (2007), Melek Acar Boyacioglu (2009), Yang Zijiang (2011, and so on who combined the SVW and other methods.…”

Section: Introductionmentioning

confidence: 99%

Study on Early Warning of Enterprise Financial Distress — Based on Partial Least-squares Logistic Regression

Zhao

Bao

2015

Acta Oeconomica

View full text Add to dashboard Cite

Establishment of an effective early warning system can make the company operators make relevant decisions as soon as possible when fi nding the crisis, improve the operating results and fi nancial condition of enterprise, and can also make investors avoid or reduce investment losses. This paper applies the partial least-squares logistic regression model for the analysis on early warning of enterprise fi nancial distress in consideration of quite sensitive characteristics of common logistic model for the multicollinearity. The data of real estate industry listed companies in China are used to compare and analyze the early warning of fi nancial distress by using the logistic model and the partial least-squares logistic model, respectively. The study results show that compared with the common logistic regression model, the applicability of partial least-squares logistic model is stronger due to its eliminating multicollinearity problem among various early warning indicators.

show abstract

Hybrid genetic algorithms and support vector machines for bankruptcy prediction

Cited by 336 publications

References 23 publications

Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy

Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy

A genetically optimized level set approach to segmentation of thyroid ultrasound images

Study on Early Warning of Enterprise Financial Distress — Based on Partial Least-squares Logistic Regression

Contact Info

Product

Resources

About