Universal approximation using radial-basis-function networks
Neural Computation
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Credit Risk Scorecards: Developing And Implementing Intelligent Credit Scoring
Credit Risk Scorecards: Developing And Implementing Intelligent Credit Scoring
Application of support vector machines to corporate credit rating prediction
Expert Systems with Applications: An International Journal
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
Credit risk assessment with a multistage neural network ensemble learning approach
Expert Systems with Applications: An International Journal
Deterministic neural classification
Neural Computation
Review: Neural networks and statistical techniques: A review of applications
Expert Systems with Applications: An International Journal
Sales forecasting using extreme learning machine with applications in fashion retailing
Decision Support Systems
No-reference image quality assessment using modified extreme learning machine classifier
Applied Soft Computing
Expert Systems with Applications: An International Journal
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
Error minimized extreme learning machine with growth of hidden nodes and incremental learning
IEEE Transactions on Neural Networks
Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
The Listed Company's Credit Rating Based on Logistic Regression Model Add Non-financial Factors
WMSVM '10 Proceedings of the 2010 Second International Conference on Modeling, Simulation and Visualization Methods
Realtime training on mobile devices for face recognition applications
Pattern Recognition
Municipal credit rating modelling by neural networks
Decision Support Systems
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Computers and Operations Research
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
| Hi-index | 0.01 |
Corporate credit ratings are one of the key problems of the credit risk management, which has attracted much research attention since the credit crisis in 2007. Scorecards are the most widely used approaches for corporate credit ratings nowadays. However, they have heavy dependency on the involvement of users. AI technologies, such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) have demonstrated their remarkable performance on automatic corporate credit ratings. Corporate credit ratings involve various rating models, and their outputs can scale to multiple levels and be used for various applications. Such inherent complexity gives rise to the requirement of higher demands on the effectiveness of learning algorithms regarding the accuracy, overfitness, error distribution, and output distribution. Most research works show that SVMs have better performance than ANNs on accuracy. This paper carries out a comprehensive experimental comparison study over the effectiveness of four learning algorithms, i.e., BP, ELM, I-ELM, and SVM over a data set consisting of real financial data for corporate credit ratings. The results are presented and discussed in the paper.