Automating the Design of Data Mining Algorithms with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Freitas:2013:NICSO,

• author = "Alex A. Freitas",
• title = "Automating the Design of Data Mining Algorithms with Genetic Programming",
• booktitle = "VI International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO 2013)",
• year = "2013",
• editor = "German Terrazas and Fernando Esteban Barril Otero and Antonio D. Masegosa",
• volume = "512",
• series = "Studies in Computational Intelligence",
• pages = "ix",
• address = "Canterbury, United Kingdom",
• month = sep # " 2-4",
• publisher = "Springer",
• note = "Plenary Talk",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-319-01691-7",
• URL = "http://link.springer.com/content/pdf/bfm%3A978-3-319-01692-4%2F1.pdf",
• DOI = "doi:10.1007/978-3-319-01692-4",
• size = "0.5 pages",
• abstract = "Rule induction and decision-tree induction algorithms are among the most popular types of classification algorithms in the field of data mining. Research on these two types of algorithms produced many new algorithms in the last 30 years. However, all the rule induction and decision-tree induction algorithms created over that period have in common the fact that they have been manually designed, typically by incrementally modifying a few basic rule induction or decision-tree induction algorithms. Having these basic algorithms and their components in mind, we describe the use of Genetic Programming (GP), a type of evolutionary algorithm that automatically creates computer programs, to automate the process of designing rule induction and decision-tree induction algorithms. The basic motivation is to automatically create complete rule induction and decision-tree induction algorithms in a data-driven way, trying to avoid the human biases and preconceptions incorporated in manually-designed algorithms. Two proposed GP methods (one for evolving rule induction algorithms, the other for evolving decision-tree induction algorithms) are evaluated on a number of datasets, and the results show that the machine-designed rule induction and decision-tree induction algorithms are competitive with well-known human-designed algorithms of the same type.",
• notes = "1 page abstract only \cite{Pappa:AEDMA}

  See also \cite{Vanneschi:2013:NICSO}. NICSO 2013 http://www.nicso2013.org/programme.html http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-319-01691-7",

}

Genetic Programming entries for Alex Alves Freitas

Citations