International Journal of Man-Machine Studies - Knowledge acquisition for knowledge-based systems, part 1. Based on an AAAI work
Reinforcement learning architectures for animats
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Induction of fuzzy decision trees
Fuzzy Sets and Systems
A genetic algorithm for generating fuzzy classification rules
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Combining Multiple Knowledge Bases
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Properties of the Bucket Brigade
Proceedings of the 1st International Conference on Genetic Algorithms
An Introduction to Anticipatory Classifier Systems
Learning Classifier Systems, From Foundations to Applications
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Evolution-assisted discovery of sentinel features in epidemiologic surveillance
Evolution-assisted discovery of sentinel features in epidemiologic surveillance
Zcs: A zeroth level classifier system
Evolutionary Computation
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Machine learning methods such as fuzzy logic, neural networks and decision tree induction have been applied to learn rules but they may be trapped into local optimal. Based on the principle of natural evolution and global searching, a genetic algorithm is promising in obtaining better results. This article adopts learning classifier systems (LCS) technique to provide a three-phase knowledge extraction methodology, which makes continues and instant learning while integrates multiple rule sets into a centralized knowledge base. This paper makes three important contributions: (1) it represents various rule sets that are derived from different sources and encoded as a fixed-length bit string in the knowledge encoding phase; (2) it uses three criteria (accuracy, coverage, and fitness) to select an optimal set of rules from a large population in the knowledge extraction phase; (3) it applies genetic operations to generate optimal rule sets in the knowledge integration phase. The experiments prove the rule sets derived by the proposed approach is more accurate than other machine learning algorithm.