Improving the Efficiency of Inductive Logic Programming Systems
Nuno A. Fonseca, VĂtor Santos Costa, Ricardo Rocha, Rui Camacho and Fernando Silva
2009
Abstract
Inductive Logic Programming (ILP) is a sub-field of Machine Learning
that provides an excellent framework for Multi-Relational Data Mining
applications. The advantages of ILP have been successfully
demonstrated in complex and relevant industrial and scientific
problems. However, to produce valuable models, ILP systems often
require long running times and large amounts of memory. In this
article we address fundamental issues that have direct impact on the
efficiency of ILP systems. Namely, we discuss how improvements in the
indexing mechanisms of an underlying Logic Programming system benefit
ILP performance. Furthermore, we propose novel data structures to
reduce memory requirements and we suggest a new lazy evaluation
technique to search the hypothesis space more efficiently. These
proposals have been implemented in the April ILP system and evaluated
using several well known data sets. The results observed show
significant improvements in running time without compromising the
accuracy of the models generated. Indeed, the combined techniques
achieve several order of magnitudes speedup in some data
sets. Moreover, memory requirements are reduced in nearly half of the
data sets.
Bibtex
@Article{fonseca-spe09,
author = {N. A. Fonseca and V. Santos Costa and R. Rocha and R. Camacho and F. Silva},
title = {{Improving the Efficiency of Inductive Logic Programming Systems}},
journal = {Software: Practice and Experience},
pages = {189--219},
volume = {39},
number = {2},
year = {2009},
}
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Wiley InterScience