ILP systems induce first-order clausal theories performing a search through very large hypotheses spaces containing redundant hypotheses. The generation of redundant hypotheses may prevent the systems from finding good models and increases the time to induce them. In this paper we propose a classification of hypotheses redundancy and show how expert knowledge can be provided to an ILP system to avoid it. Experimental results show that the number of hypotheses generated and execution time are reduced when expert knowledge is used to avoid redundancy. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Fonseca, N., Costa, V. S., Silva, F., & Camacho, R. (2004). On avoiding redundancy in inductive logic programming. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3194, pp. 132–146). Springer Verlag. https://doi.org/10.1007/978-3-540-30109-7_13
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