Abstract
The algorithm extracts propositional rules from a labeled data set. The constituent parts of a rule are the features of the labeled data-set, each accompanied by an appropriate interval of activation and a label denoting the class. Initially, the input space is partitioned using tiles. The algorithm tries to compose the largest possible orthogonal intervals out of tiles. After the creation of intervals for each feature the rule receives credit for its classification ability. This credit will be used to improve the rule. We have obtained encouraging results on 5 different classification problems: the iris data set, the concentric data, the four gaussians, the pima-indians set and the image segmentation data set.
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© 2001 Springer-Verlag Wien
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Vogiatzis, D., Stafylopatis, A. (2001). Reinforcement Learning for Rule Generation. In: Kůrková, V., Neruda, R., Kárný, M., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6230-9_23
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DOI: https://doi.org/10.1007/978-3-7091-6230-9_23
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