Discovering New Rule Induction Algorithms with Grammar-based Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InCollection{series/springer/PappaF08,

• title = "Discovering New Rule Induction Algorithms with Grammar-based Genetic Programming",
• author = "Gisele L. Pappa and Alex Alves Freitas",
• bibdate = "2008-12-16",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/series/springer/MaimonRokach2008.html#PappaF08",
• booktitle = "Soft Computing for Knowledge Discovery and Data Mining",
• publisher = "Springer",
• year = "2008",
• editor = "Oded Maimon and Lior Rokach",
• isbn13 = "978-0-387-69934-9",
• pages = "133--152",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1007/978-0-387-69935-6_6",
• abstract = "Rule induction is a data mining technique used to extract classification rules of the form IF (conditions) THEN (predicted class) from data. The majority of the rule induction algorithms found in the literature follow the sequential covering strategy, which essentially induces one rule at a time until (almost) all the training data is covered by the induced rule set. This strategy describes a basic algorithm composed by several key elements, which can be modified and/or extended to generate new and better rule induction algorithms. With this in mind, this work proposes the use of a grammar-based genetic programming (GGP) algorithm to automatically discover new sequential covering algorithms. The proposed system is evaluated using 20 data sets, and the automatically-discovered rule induction algorithms are compared with four well-known human-designed rule induction algorithms. Results showed that the GGP system is a promising approach to effectively discover new sequential covering algorithms.",
• notes = "26 rules in grammar: table 1",
• size = "20 pages",

}

Genetic Programming entries for Gisele L Pappa Alex Alves Freitas

Citations