Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Evolutionary multiobjective optimization for the design of fuzzy rule-based ensemble classifiers
International Journal of Hybrid Intelligent Systems - Hybrid Intelligent systems in Ensembles
Genetic rule selection with a multi-classifier coding scheme for ensemble classifier design
International Journal of Hybrid Intelligent Systems - Hybridization of Intelligent Systems
MOBAIS: A Bayesian Artificial Immune System for Multi-Objective Optimization
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Feature Subset Selection by Means of a Bayesian Artificial Immune System
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
BAIS: A Bayesian Artificial Immune System for the effective handling of building blocks
Information Sciences: an International Journal
IEEE Transactions on Fuzzy Systems
A proposal on reasoning methods in fuzzy rule-based classification systems
International Journal of Approximate Reasoning
IEEE Transactions on Fuzzy Systems
International Journal of Hybrid Intelligent Systems - Hybrid Fuzzy Models
Designing fuzzy-rule-based systems using continuous ant-colony optimization
IEEE Transactions on Fuzzy Systems
Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures
Information Sciences: an International Journal
Designing ensembles of fuzzy classification systems: an immune-inspired approach
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Learning Ensembles of Neural Networks by Means of a Bayesian Artificial Immune System
IEEE Transactions on Neural Networks
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In this paper we perform a deep investigation about the usefulness of an immune-inspired algorithm to design accurate and compact fuzzy rule bases for classification problems. The algorithm, called Bayesian Artificial Immune System BAIS, incorporates a mechanism to learn a probability graphical model from the promising solutions found so far. Thus, BAIS utilizes this model to sample new candidate solutions. The probabilistic model utilized here is a Bayesian network due to its capability of expressing the relationships among the variables of the problem, avoiding the disruption of already obtained high-quality partial solutions building blocks. Besides the capability to identify and manipulate building blocks, the algorithm maintains diversity in the population, performs multimodal optimization and adjusts the size of the population automatically according to the problem. These attributes are generally absent from alternative algorithms, and can be considered useful attributes when generating fuzzy rule bases, thus guiding to high-performance classifiers. BAIS was evaluated in thirteen well-known classification problems and its performance compares favorably with that produced by contenders.