Note on free lunches and cross-validation
Neural Computation
General and Efficient Multisplitting of Numerical Attributes
Machine Learning
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Combining GP operators with SA search to evolve fuzzy rule based classifiers
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Learning fuzzy classification rules from labeled data
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
An integrated fuzzy cells-classifier
Image and Vision Computing
Expert Systems with Applications: An International Journal
A weighting function for improving fuzzy classification systems performance
Fuzzy Sets and Systems
Pattern Recognition Letters
Weighting fuzzy classification rules using receiver operating characteristics (ROC) analysis
Information Sciences: an International Journal
A PSO-aided neuro-fuzzy classifier employing linguistic hedge concepts
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Advanced Engineering Informatics
Automatic tuning of complex fuzzy systems with Xfuzzy
Fuzzy Sets and Systems
Fuzzy-rough nearest neighbor algorithms in classification
Fuzzy Sets and Systems
Fuzzy classifier design using genetic algorithms
Pattern Recognition
Robust fuzzy relational classifier incorporating the soft class labels
Pattern Recognition Letters
A proposed method for learning rule weights in fuzzy rule-based classification systems
Fuzzy Sets and Systems
A proposal on reasoning methods in fuzzy rule-based classification systems
International Journal of Approximate Reasoning
A learning based self-organized additive fuzzy clustering method and its application for EEG data
International Journal of Knowledge-based and Intelligent Engineering Systems - Intelligent Information Processing: Techniques and Applications
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Different approaches to design fuzzy rule-based classification systems can be grouped into two main categories: descriptive and accurate. In the descriptive category, the emphasis is on the interpretability of the resulting classifier. The classifier is usually represented by a compact set of short fuzzy rules (i.e., with a few number of antecedent conditions) that make it a suitable tool for knowledge representation. In the accurate category, the generalization ability of the classifier is the main target in the design process and no attempt is made to use understandable fuzzy rules in constructing the rule base. In this paper, we propose a simple and efficient method to construct an accurate fuzzy classification system. We use rule-weight as a simple mechanism to tune the classifier and propose a new method of rule-weight specification for this purpose. Through computer simulations on some data sets from UCI repository, we show that the proposed scheme achieves better prediction accuracy compared with other fuzzy and non-fuzzy rule-based classification systems proposed in the past.