Abstract
The key of using genetic algorithm to mine first-order rules is how to precisely evaluate the quality of first-order rules. By adopting the concept of binding and information theory, a new fitness function based on information gain is proposed. The new fitness function not only measures the quality of first-order rules precisely but also solves the equivalence class problem, which exists in the common evaluation criteria based on the number of examples covered by rules.
Cite
CITATION STYLE
Yang, X., Liu, C., & Zhong, N. (2003). First-order rules mining guided by information gain. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2871, pp. 463–467). Springer Verlag. https://doi.org/10.1007/978-3-540-39592-8_65
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