International Journal of Man-Machine Studies - Knowledge acquisition for knowledge-based systems, part 1. Based on an AAAI work
Induction of fuzzy decision trees
Fuzzy Sets and Systems
A genetic algorithm for generating fuzzy classification rules
Fuzzy Sets and Systems
Combining Multiple Knowledge Bases
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
An Introduction to Anticipatory Classifier Systems
Learning Classifier Systems, From Foundations to Applications
Evolution-assisted discovery of sentinel features in epidemiologic surveillance
Evolution-assisted discovery of sentinel features in epidemiologic surveillance
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Machine learning methods such as fuzzy logic, neural networks and decision tree induction have been applied to learn rules, however they can get trapped into a local optimal. Based on the principle of natural evolution and global searching, a genetic algorithm is promising for obtaining better results. This article adopts the learning classifier systems (LCS) technique to provide a hybrid knowledge integration strategy, which makes for continuous and instant learning while integrating multiple rule sets into a centralized knowledge base. This paper makes three important contributions: (1) it provides a knowledge encoding methodology to represent various rule sets that are derived from different sources, and that are encoded as a fixed-length bit string; (2) it proposes a knowledge integration methodology to apply genetic operations and credit assignment to generate optimal rule sets; (3) it uses three criteria (accuracy, coverage, and fitness) to apply the knowledge extraction process, which is very effective in selecting an optimal set of rules from a large population. The experiments prove that the rule sets derived by the proposed approach is more accurate than the Fuzzy ID3 algorithm.