Abstract
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.
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Chen, AP., Chen, MY. (2005). An Implementation of Learning Classifier Systems for Rule-Based Machine Learning. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552451_7
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DOI: https://doi.org/10.1007/11552451_7
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