School of Computing

Automatically evolving rule induction algorithms

G. L. Pappa and A. A. Freitas

In Johannes Fuernkranz, Tobias Scheffer, and Myra Spiliopoulou, editors, Proc. of the 17th European Conference on Machine Learning, volume 4212/2006 of Lecture Notes in Computer Science, pages 182-196, Berlin, September 2006. Springer Berlin / Heidelberg.

Abstract

Research in the rule induction algorithm field produced many algorithms in the last 30 years. However, these algorithms are usually obtained from a few basic rule induction algorithms that have been often changed to produce better ones. Having these basic algorithms and their components in mind, this work proposes the use of Grammar-based Genetic Programming (GGP) to automatically evolve rule induction algorithms. The proposed GGP is evaluated in extensive computational experiments involving 11 data sets. Overall, the results show that effective rule induction algorithms can be automatically generated using GGP. The automatically evolved rule induction algorithms were shown to be competitive with well-known manually designed ones. The proposed approach of automatically evolving rule induction algorithms can be considered a pioneering one, opening a new kind of research area.

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Bibtex Record

@inproceedings{2434,
author = {G. L. Pappa and A. A. Freitas},
title = {Automatically Evolving Rule Induction Algorithms},
month = {September},
year = {2006},
pages = {182-196},
keywords = {determinacy analysis, Craig interpolants},
note = {},
doi = {},
url = {http://www.cs.kent.ac.uk/pubs/2006/2434},
    publication_type = {inproceedings},
    submission_id = {11590_1159292609},
    ISBN = {978-3-540-45375-8},
    booktitle = {Proc. of the 17th European Conference on Machine Learning },
    editor = {Johannes Fuernkranz and Tobias Scheffer and Myra Spiliopoulou},
    volume = {4212/2006},
    series = {Lecture Notes in Computer Science},
    address = {Berlin},
    publisher = {Springer Berlin / Heidelberg},
    ISSN = {0302-9743},
    refereed = {yes},
}

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