Learning maximal structure rules in fuzzy logic for knowledge acquisition in expert systems
Fuzzy Sets and Systems
Processing individual fuzzy attributes for fuzzy rule induction
Fuzzy Sets and Systems
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Machine Learning
A spectrum of compromise aggregation operators for multi-attribute decision making
Artificial Intelligence
Classifier fitness based on accuracy
Evolutionary Computation
Local distance-based classification
Knowledge-Based Systems
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Development and Verification of Rule Based Systems -- A Survey of Developers
RuleML '08 Proceedings of the International Symposium on Rule Representation, Interchange and Reasoning on the Web
Learning to classify with missing and corrupted features
Machine Learning
A highly adaptive recommender system based on fuzzy logic for B2C e-commerce portals
Expert Systems with Applications: An International Journal
OCULUS surveillance system: Fuzzy on-line speed analysis from 2D images
Expert Systems with Applications: An International Journal
A dissimilarity measure for the k-Modes clustering algorithm
Knowledge-Based Systems
Differences between t-norms in fuzzy control
International Journal of Intelligent Systems
Control and learning of ambience by an intelligent building
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
SLAVE: a genetic learning system based on an iterative approach
IEEE Transactions on Fuzzy Systems
Self-organized fuzzy system generation from training examples
IEEE Transactions on Fuzzy Systems
Constructing fuzzy models with linguistic integrity from numerical data-AFRELI algorithm
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
FRIwE: fuzzy rule identification with exceptions
IEEE Transactions on Fuzzy Systems
Generating an interpretable family of fuzzy partitions from data
IEEE Transactions on Fuzzy Systems
A robust design criterion for interpretable fuzzy models with uncertain data
IEEE Transactions on Fuzzy Systems
Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing
IEEE Transactions on Fuzzy Systems
KEMNAD: A Knowledge Engineering Methodology For Negotiating Agent Development
Computational Intelligence
A D-S theory based AHP decision making approach with ambiguous evaluations of multiple criteria
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
A Model for Decision Making with Missing, Imprecise, and Uncertain Evaluations of Multiple Criteria
International Journal of Intelligent Systems
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There are a lot of systems that make decisions or classifications on the basis of a number of rules. This set of rules that govern such a system is called the rule base. When a new system of this kind is being developed, setting up its rule base is a time-consuming and expensive process because the rule base contains the knowledge of the outside world, which could be acquired from experts or produced from previous experiences. In this latter case, machine-learning algorithms can help. In fact, many methods have been proposed to generate rules from training instances. The aim of this paper is to present a new fuzzy learning algorithm to generate IF-THEN rules, for classifying instances in one application domain. This algorithm can improve the results offered by a previously presented algorithm. In addition, the more common classification problems of the original algorithm are presented and a measure to determine the conflicts among generated rules is introduced. Moreover, we study the classification stage of that inductive fuzzy learning algorithm and an improvement is suggested to obtain better classification results.