Genetic Relational Search for Inductive Concept Learning

ISBN-13: 9783843355483 / Angielski / Miękka / 2010 / 192 str.

Federico Divina

Opis

Learning from examples in First Order Logic, also known as Inductive Logic Programming (ILP), constitutes a central topic in Machine Learning, with relevant applications to problems in complex domains, e.g., natural language and computational biology. Learning can be viewed as a search problem in the space of all possible hypotheses. Given a background knowledge, a set of positive examples and a set of negative examples, expressed in First Order Logic, one has to find a hypothesis which covers all positive examples and none of the negative ones. This problem is NP-hard even if the language to represent hypotheses is propositional logic. When FOL hypotheses are used, this complexity is combined with the complexity of evaluating hypotheses. This book describes an evolutionary algorithm for ILP. The algorithm, called ECL (for Evolutionary Concept Learner), evolves a population of Horn clauses by repeated selection, mutation and optimization of more fit clauses. ECL relies on four greedy mutation operators for searching the hypothesis space, and employs an optimization phase that follows each mutation. Experimental results show that ECL works well in practice.

O autorze

Federico Divina graduated in Computer Science at the Ca' Foscari University of Venice, Italy. He received his Ph.D. in Artificial Intelligence by the Free University of Amsterdam, The Netherlands. He worked as a post-doc at the Tilburg University and is currently an assistant professor at Pablo de Olavide University of Seville, Spain.