Inter-company comparison using modified TOPSIS with objective weights
Computers and Operations Research
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Extracting Trees from Trained SVM Models using a TREPAN Based Approach
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
An application of AHP and sensitivity analysis for selecting the best slicing machine
Computers and Industrial Engineering
Engineering Applications of Artificial Intelligence
The evidence framework applied to classification networks
Neural Computation
Moving towards efficient decision tree construction
Information Sciences: an International Journal
Automatic EEG signal classification for epilepsy diagnosis with Relevance Vector Machines
Expert Systems with Applications: An International Journal
Information Processing and Management: an International Journal
Combining neural networks and semantic feature space for email classification
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Artificial Intelligence Review
Information Sciences: an International Journal
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting
Knowledge-Based Systems
Mining data with random forests: A survey and results of new tests
Pattern Recognition
Rule extraction from support vector machines: A review
Neurocomputing
An empirical study of classification algorithm evaluation for financial risk prediction
Applied Soft Computing
Municipal credit rating modelling by neural networks
Decision Support Systems
Two credit scoring models based on dual strategy ensemble trees
Knowledge-Based Systems
ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging
Knowledge-Based Systems
Neural network demand models and evolutionary optimisers for dynamic pricing
Knowledge-Based Systems
Improved hierarchical fuzzy TOPSIS for road safety performance evaluation
Knowledge-Based Systems
Comparison of weights in TOPSIS models
Mathematical and Computer Modelling: An International Journal
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Multiple extreme learning machines for a two-class imbalance corporate life cycle prediction
Knowledge-Based Systems
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The sub-prime mortgage crisis of 2007 and the global financial tsunami of 2008 have undermined worldwide economic stability. Consequently, the timely analysis of credit risk has become more essential than ever before. The performance of early risk warning mechanisms may vary according to the criteria used and the underlying environment. This study establishes numerous criteria to assess the performance of classifiers and introduces a multiple criteria decision making method to determine suitable warning mechanisms. After undergoing these evaluations, the enhanced decision support model (EDSM), which incorporates the relevance vector machine with decision tree, is proposed. A decision tree is employed to overcome the opaque nature of the relevance vector machine; it yields knowledge as logical statements and aids in the interpretability of the forecasting results. The advantages of the EDSM involve overcoming the timeliness problem, fostering faster credit financing decisions, diminishing possible mistakes and reducing the credit analysis cost. This study also examines the feasibility of corporate transparency and the information disclosure (TD) criterion during an upturn in the economy, and finds that this procedure presents a suitable policy-relevant direction for regulators to design future measurements. Finally, this study shows that the EDSM is a promising way for investors, creditors, bankers and regulators to analyze credit rating domains.