C4.5: programs for machine learning
C4.5: programs for machine learning
Rough set algorithms in classification problem
Rough set methods and applications
A case-based approach using inductive indexing for corporate bond rating
Decision Support Systems - Decision-making and E-commerce systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Creating Effective Load Models for Performance Testing with Incomplete Empirical Data
WSE '04 Proceedings of the Web Site Evolution, Sixth IEEE International Workshop
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
Soft computing system for bank performance prediction
Applied Soft Computing
Review: Dimensionality reduction based on rough set theory: A review
Applied Soft Computing
Using Wikipedia knowledge to improve text classification
Knowledge and Information Systems
On classification and segmentation of massive audio data streams
Knowledge and Information Systems
Knowledge and Information Systems
A study of Taiwan's issuer credit rating systems using support vector machines
Expert Systems with Applications: An International Journal
Multi knowledge based rough approximations and applications
Knowledge-Based Systems
Simple instance selection for bankruptcy prediction
Knowledge-Based Systems
Jmax-pruning: A facility for the information theoretic pruning of modular classification rules
Knowledge-Based Systems
Bankruptcy prediction models based on multinorm analysis: An alternative to accounting ratios
Knowledge-Based Systems
A hybrid KMV model, random forests and rough set theory approach for credit rating
Knowledge-Based Systems
Bipolar fuzzy rough set model on two different universes and its application
Knowledge-Based Systems
A New Version of the Rule Induction System LERS
Fundamenta Informaticae
An extended fuzzy measure on competitiveness correlation based on WCY 2011
Knowledge-Based Systems
Computers in Biology and Medicine
A method for extracting rules from spatial data based on rough fuzzy sets
Knowledge-Based Systems
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Banks are important to national, and even global, economic stability. Banking panics that follow bank insolvency or bankruptcy, especially of large banks, can severely jeopardize economic stability. Therefore, issuers and investors urgently need a credit rating indicator to help identify the financial status and operational competence of banks. A credit rating provides financial entities with an assessment of credit worthiness, investment risk, and default probability. Although numerous models have been proposed to solve credit rating problems, they have the following drawbacks: (1) lack of explanatory power; (2) reliance on the restrictive assumptions of statistical techniques; and (3) numerous variables, which result in multiple dimensions and complex data. To overcome these shortcomings, this work applies two hybrid models that solve the practical problems in credit rating classification. For model verification, this work uses an experimental dataset collected from the Bankscope database for the period 1998-2007. Experimental results demonstrate that the proposed hybrid models for credit rating classification outperform the listing models in this work. A set of decision rules for classifying credit ratings is extracted. Finally, study findings and managerial implications are provided for academics and practitioners.