International Journal of Man-Machine Studies
The nature of statistical learning theory
The nature of statistical learning theory
On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
Theoretical Computer Science
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
Feature selection for the SVM: An application to hypertension diagnosis
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A SVM-based cursive character recognizer
Pattern Recognition
Feature Selection Algorithms Using Rough Set Theory
ISDA '07 Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications
International Journal on Document Analysis and Recognition
Artificial Intelligence in Medicine
Support vector machines for credit scoring and discovery of significant features
Expert Systems with Applications: An International Journal
Building credit scoring models using genetic programming
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fast text categorization using concise semantic analysis
Pattern Recognition Letters
An application of locally linear model tree algorithm for predictive accuracy of credit scoring
MEDI'11 Proceedings of the First international conference on Model and data engineering
On the use of data filtering techniques for credit risk prediction with instance-based models
Expert Systems with Applications: An International Journal
Assessing scorecard performance: A literature review and classification
Expert Systems with Applications: An International Journal
Advanced Engineering Informatics
Hi-index | 12.09 |
The credit scoring has been regarded as a critical topic and its related departments make efforts to collect huge amount of data to avoid wrong decision. An effective classificatory model will objectively help managers instead of intuitive experience. This study proposes four approaches combining with the SVM (support vector machine) classifier for features selection that retains sufficient information for classification purpose. Different credit scoring models are constructed by selecting attributes with four approaches. Two UCI (University of California, Irvine) data sets are chosen to evaluate the accuracy of various hybrid-SVM models. SVM classifier combines with conventional statistical LDA, Decision tree, Rough sets and F-score approaches as features pre-processing step to optimize feature space by removing both irrelevant and redundant features. In this paper, the procedure of the proposed approaches will be described and then evaluated by their performances. The results are compared in combination with SVM classifier and nonparametric Wilcoxon signed rank test will be held to show if there is any significant difference between these models. The result in this study suggests that hybrid credit scoring approach is mostly robust and effective in finding optimal subsets and is a promising method to the fields of data mining.