Please use this identifier to cite or link to this item: http://hdl.handle.net/10216/67388
Author(s): Nuno A. Fonseca
Ricardo Rocha
Rui Camacho
Vítor Santos Costa
Title: ILP: Compute Once, Reuse Often
Issue Date: 2007
Abstract: Inductive Logic Programming (ILP) is a powerful and welldeveloped abstraction for multi-relational data mining techniques. However, ILP systems are not particularly fast, most of their execution time is spent evaluating the hypotheses they construct. The evaluation time needed to assess the quality of each hypothesis depends mainly on the number of examples and the theorem proving effort required to determine if an example is entailed by the hypothesis. We propose a technique that reduces the theorem proving effort to a bare minimum and stores valuable information to compute the number of examples entailed by each hypothesis (using a tree data structure). The information is computed only once (pre-compiled) per example. Evaluation of hypotheses requires only basic and efficient operations on trees. This proposal avoids re-computation of hypothesis¿ value in theory-level search and cross-validation algorithms, whenever the same data set is used with different parameters. In an empirical evaluation the technique yielded considerable speedups.
Subject: Engenharia do conhecimento, Engenharia electrotécnica, electrónica e informática
Call Number: 64310
URI: http://hdl.handle.net/10216/67388
Source: 6th Workshop on Multi-Relational Data Mining (MRDM 2007)
Document Type: Artigo em Livro de Atas de Conferência Internacional
Rights: openAccess
License: https://creativecommons.org/licenses/by-nc/4.0/
Appears in Collections:FEUP - Artigo em Livro de Atas de Conferência Internacional

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