The nature of statistical learning theory
The nature of statistical learning theory
Pairwise classification and support vector machines
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A case-based approach using inductive indexing for corporate bond rating
Decision Support Systems - Decision-making and E-commerce systems
AI Game Programming Wisdom
A Simple Decomposition Method for Support Vector Machines
Machine Learning
On the Learnability and Design of Output Codes for Multiclass Problems
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
The Journal of Machine Learning Research
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
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Bond rating using support vector machine
Intelligent Data Analysis
Application of support vector machines to corporate credit rating prediction
Expert Systems with Applications: An International Journal
Letters: Compact multi-class support vector machine
Neurocomputing
Expert Systems with Applications: An International Journal
Robust and efficient multiclass SVM models for phrase pattern recognition
Pattern Recognition
A multi-agent-based model for a negotiation support system in electronic commerce
Enterprise Information Systems
A study on X party material flow: the theory and applications
Enterprise Information Systems
Electronic supply chain management applications by Swedish SMEs
Enterprise Information Systems
Electronic marketplace definition and classification: literature review and clarifications
Enterprise Information Systems
Multiclass support vector machines for diagnosis of erythemato-squamous diseases
Expert Systems with Applications: An International Journal
Computers & Mathematics with Applications
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
A study of Taiwan's issuer credit rating systems using support vector machines
Expert Systems with Applications: An International Journal
A survey of software adaptation in mobile and ubiquitous computing
Enterprise Information Systems
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Global optimization of support vector machines using genetic algorithms for bankruptcy prediction
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Interpolation representation of feedforward neural networks
Mathematical and Computer Modelling: An International Journal
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
A hybrid KMV model, random forests and rough set theory approach for credit rating
Knowledge-Based Systems
Intelligent prescription-diagnosis function for rehabilitation training robot system
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part II
Expert Systems with Applications: An International Journal
Exploitation of pairwise class distances for ordinal classification
Neural Computation
International Journal of Mobile Learning and Organisation
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Predicting corporate credit-rating using statistical and artificial intelligence (AI) techniques has received considerable research attention in the literature. In recent years, multi-class support vector machines (MSVMs) have become a very appealing machine-learning approach due to their good performance. Until now, researchers have proposed a variety of techniques for adapting support vector machines (SVMs) to multi-class classification, since SVMs were originally devised for binary classification. However, most of them have only focused on classifying samples into nominal categories; thus, the unique characteristic of credit-rating - ordinality - seldom has been considered in the proposed approaches. This study proposes a new type of MSVM classifier (named OMSVM) that is designed to extend the binary SVMs by applying an ordinal pairwise partitioning (OPP) strategy. Our model can efficiently and effectively handle multiple ordinal classes. To validate OMSVM, we applied it to a real-world case of bond rating. We compared the results of our model with those of conventional MSVM approaches and other AI techniques including MDA, MLOGIT, CBR, and ANNs. The results showed that our proposed model improves the performance of classification in comparison to other typical multi-class classification techniques and uses fewer computational resources.