- author = "Gisele L. Pappa and Alex Alves Freitas",
- title = "Automatically Evolving Rule Induction Algorithms",
- booktitle = "Proceedings of the 17th European Conference on Machine Learning, ECML 2006",
- editor = "Johannes F{\"u}rnkranz and Tobias Scheffer and Myra Spiliopoulou",
- publisher = "Springer",
- series = "Lecture Notes in Computer Science",
- volume = "4212",
- year = "2006",
- pages = "341--352",
- address = "Berlin",
- month = sep # " 18-22",
- bibsource = "DBLP, http://dblp.uni-trier.de",
- keywords = "genetic algorithms, genetic programming, rule induction algorithms, data mining, grammar-based genetic programming",
- ISSN = "0302-9743",
- ISBN = "3-540-45375-X",
-
DOI = "
doi:10.1007/11871842_34",
- size = "12 pages",
- 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.",
- refereed = "yes",
-
notes = "
26 rules in grammar. Parse tree representation.",
