Optimising FOIL by new scoring functions

Abstract

FOIL is an Inductive Logic Programming Algorithm to dis cover first order rules to explain the patterns involved in a domain of knowledge. Domains with a huge amount of information are handicaps for FOIL due to the explosion of the search of space to devise the rules. Current solutions to problems in these domains are restricted to devising ad hoc domain dependent inductive algorithms that use a less-expressive formalism to code rules. We work on optimising FOIL learning process to deal with such complex domain problems while retaining expressiveness. Our hypothesis is that changing the Information Gain scoring function, used by FOIL to de cide how rules are learnt, can reduce the number of steps the algorithm performs. We have analysed 15 scoring functions, normalised them into a common notation and checked a test in which they are computed. The learning process will be evaluated according to its efficiency, and the quality of the rules according to their precision, recall, complexity and specificity. The results reinforce our hypothesis, demonstrating that replacing the Information Gain can optimise both the FOIL algorithm execution and the learnt rules.