Affiliations: Instituto de Biologia Molecular e Celular (IBMC) &
CRACS, University of Porto, Portugal. [email protected] | FEUP & LIAAD, University of Porto, Portugal.
[email protected] | DCC-FCUP & CRACS, University of Porto, Portugal.
[email protected]; [email protected]
Note: [] Address for correspondence: IBMC, AC Nuno Fonseca, Rua do Campo
Alegre 823, 4150-180 Porto, Portugal
Abstract: Inductive Logic Programming (ILP) is a powerful and well-developed
abstraction for multi-relational data mining techniques. Despite the
considerable success of ILP, deployed ILP systems still have efficiency
problems when applied to complex problems. In this paper we propose a novel
technique that avoids the procedure of deducing each example to evaluate each
constructed clause. The technique is based on the Mode Directed Inverse
Entailment approach to ILP, where a bottom clause is generated for each example
and the generated clauses are subsets of the literals of such bottom clause. We
propose to store in a prefix-tree all clauses that can be generated from all
bottom clauses together with some extra information. We show that this
information is sufficient to estimate the number of examples that can be
deduced froma clause and present an ILP algorithmthat exploits this
representation. We also present an extension of the algorithm where each
prefix-tree is computed only once (compiled) per example. The evaluation of
hypotheses requires only basic and efficient operations on trees. This proposal
avoids re-computation of hypothesis' value in theorylevel search,
in cross-validation evaluation procedures and in parameter tuning. Both
proposals are empirically evaluated on real applications and considerable
speedups were observed.
Keywords: Mode Directed Inverse Entailment, Efficiency, Data Structures, Compilation
