A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{bojarczuk:2004:EMBM,
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author = "Celia C. Bojarczuk and Heitor S. Lopes and
Alex A. Freitas and Edson L Michalkiewicz",
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title = "A constrained-syntax genetic programming system for
discovering classification rules: application to
medical data sets",
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journal = "Artificial Intelligence in Medicine",
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year = "2004",
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volume = "30",
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number = "1",
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pages = "27--48",
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month = jan,
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ISSN = "0933-3657",
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keywords = "genetic algorithms, genetic programming, data mining,
classification, medical applications",
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URL = "https://www.cs.kent.ac.uk/people/staff/aaf/pubs.html",
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URL = "http://www.cpgei.cefetpr.br/~hslopes/publicacoes/2004/aim2004.pdf",
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URL = "http://www.sciencedirect.com/science/article/B6T4K-4B42BDH-1/2/77bc597c3188977bd9ffb40ba10802ac",
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URL = "http://www.harcourt-international.com/journals/aiim/",
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DOI = "doi:10.1016/j.artmed.2003.06.001",
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abstract = "We propose a constrained-syntax genetic programming
(GP) algorithm for discovering classification rules in
medical data sets. The proposed GP contains several
syntactic constraints to be enforced by the system
using a disjunctive normal form representation, so that
individuals represent valid rule sets that are easy to
interpret. The GP is compared with C4.5, a well-known
decision-tree-building algorithm, and with another GP
that uses Boolean inputs (BGP), in five medical data
sets: chest pain, Ljubljana breast cancer, dermatology,
Wisconsin breast cancer, and pediatric adrenocortical
tumour. For this last data set a new preprocessing step
was devised for survival prediction. Computational
experiments show that, overall, the GP algorithm
obtained good results with respect to predictive
accuracy and rule comprehensibility, by comparison with
C4.5 and BGP.",
- }
Genetic Programming entries for
Celia Cristina Bojarczuk
Heitor Silverio Lopes
Alex Alves Freitas
Edson L Michalkiewicz
Citations