Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters

Min, Jae Hyung; Lee, Young‐Chan

doi:10.1016/j.eswa.2004.12.008

Cited by 731 publications

(349 citation statements)

References 66 publications

Supporting

Mentioning

338

Contrasting

Unclassified

Order By: Relevance

“…Since the successfulness of SVM depends on the choice of kernel function K and hyper parameters, a cross-validation procedure should be performed for adjusting those parameters (Min and Lee, 2005;Behzad et al, 2009). Linear, polynomial, RBF, and exponential kernels were used in our experiments, where gamma coefficient for polynomial and RBF kernel was 0.0625, degree was 3, coefficient varied from 0 to 0.1, c=10.…”

Section: Support Vector Machinesmentioning

confidence: 99%

A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

Zekić–Sušac

Pfeifer

Šarlija

2014

Business Systems Research Journal

View full text Add to dashboard Cite

Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

show abstract

Section: Support Vector Machinesmentioning

confidence: 99%

A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

Zekić–Sušac

Pfeifer

Šarlija

2014

Business Systems Research Journal

View full text Add to dashboard Cite

show abstract

“…Min and Lee [66] Factor analysis, t tests and stepwise search with criteria optimized for discriminant analysis and logistic regression…”

Section: Mlp-bpmentioning

confidence: 99%

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

Jardin

2010

Neurocomputing

111

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

“…Among the new techniques for credit scoring, SVM is one of those which generate prolific and promising results. The use of SVM in business application has been previously investigated by several works [11,12,13]. Baesens et al [14] conducted a study for benchmarking of 17 different classification techniques on eight different real-life credit datasets.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Support Vector Machine Ensemble Model for Credit Scoring

Ghodselahi¹

2011

IJCA

View full text Add to dashboard Cite

Credit risk is the most challenging risk to which financial institution are exposed. Credit scoring is the main analytical technique for credit risk assessment. In this paper a hybrid model for credit scoring is designed which applies ensemble learning for credit granting decisions. The hybrid model combines clustering and classification techniques. Ten Support Vector Machine (SVM) classifiers are utilized as the members of ensemble model. Since even a small improvement in credit scoring accuracy causes significant loss reduction, then the application of ensemble in hybrid model leads to better performance of classification. A real dataset is used to test the model performance. The test results shows that proposed hybrid SVM ensemble has better classification accuracy when compared with other methods.

show abstract

Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters

Cited by 731 publications

References 66 publications

A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

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

A Hybrid Support Vector Machine Ensemble Model for Credit Scoring

Contact Info

Product

Resources

About