Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
The nature of statistical learning theory
The nature of statistical learning theory
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
A case-based approach using inductive indexing for corporate bond rating
Decision Support Systems - Decision-making and E-commerce systems
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Feature Selection for Clustering - A Filter Solution
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
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
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Topological approaches to covering rough sets
Information Sciences: an International Journal
Rough clustering of sequential data
Data & Knowledge Engineering
Learning fuzzy rules from fuzzy samples based on rough set technique
Information Sciences: an International Journal
MMR: An algorithm for clustering categorical data using Rough Set Theory
Data & Knowledge Engineering
Feature selection in bankruptcy prediction
Knowledge-Based Systems
A study of Taiwan's issuer credit rating systems using support vector machines
Expert Systems with Applications: An International Journal
A rough set approach for selecting clustering attribute
Knowledge-Based Systems
Agent-based analysis and simulation of the consumer airline market share for Frontier Airlines
Knowledge-Based Systems
Municipal credit rating modelling by neural networks
Decision Support Systems
Using Gaussian process based kernel classifiers for credit rating forecasting
Expert Systems with Applications: An International Journal
A sequential pattern mining algorithm using rough set theory
International Journal of Approximate Reasoning
A new feature selection algorithm based on binomial hypothesis testing for spam filtering
Knowledge-Based Systems
A vague-rough set approach for uncertain knowledge acquisition
Knowledge-Based Systems
A hybrid KMV model, random forests and rough set theory approach for credit rating
Knowledge-Based Systems
On interval type-2 rough fuzzy sets
Knowledge-Based Systems
Credit risk assessment and decision making by a fusion approach
Knowledge-Based Systems
Multiple extreme learning machines for a two-class imbalance corporate life cycle prediction
Knowledge-Based Systems
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Although Asia is at the forefront of global economic growth, its investment environment is very risky and uncertain. Credit ratings are objective opinions about credit worthiness, investment risk, and default probabilities of issues or issuers. To classify credit ratings, analyze their determinants, and provide meaningful decision rules for interested parties, this work proposes an integrated procedure. First, this work adopts an integrated feature-selection approach to select key attributes, and then adopts an objective cumulative probability distribution approach (CPDA) to partition selected condition attributes by applying rough sets local-discretization cuts. This work then applies the rough sets LEM2 algorithm to generate a comprehensible set of decision rules. Finally, this work utilizes a rule filter to eliminate rules with poor support and thereby improve rule quality. The experimental focus was the Asian banking industry. Data were retrieved from a BankScope database that covers 1327 Asian banks. Experimental results demonstrate that the proposed procedure is an effective method of removing irrelevant attributes and achieving increased accuracy, providing a knowledge-based system for classification of rules for solving credit-rating problems encountered by banks, thereby benefiting interested parties.