Discovering hierarchical decision rules with evolutive algorithms in supervised learning

This paper describes a new approach, HIDER (HIerarchical DEcision Rules), for learning rules in continuous and discrete domains based on evolutive algorithms. The algorithm produces a hierarchical set of rules, that is, the rules must be applied in a speciÞc order. With this policy, the number of ru...

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Detalles Bibliográficos
Autores: Riquelme Santos, José Cristóbal, Aguilar, Jesús S., Toro Bonilla, Miguel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2000
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/100151
Acceso en línea:https://hdl.handle.net/11441/100151
Access Level:acceso abierto
Palabra clave:Evolutive Algorithms
Supervised Learning
Decision Lists
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spelling Discovering hierarchical decision rules with evolutive algorithms in supervised learningRiquelme Santos, José CristóbalAguilar, Jesús S.Toro Bonilla, MiguelEvolutive AlgorithmsSupervised LearningDecision ListsThis paper describes a new approach, HIDER (HIerarchical DEcision Rules), for learning rules in continuous and discrete domains based on evolutive algorithms. The algorithm produces a hierarchical set of rules, that is, the rules must be applied in a speciÞc order. With this policy, the number of rules may be reduced because the rules could be one inside of another. The evolutive algorithm uses both real and binary codiÞcation for the individuals of the population and introduces several new genetic operators. In addition, this paper discusses the capability of learning systems based on an evolutive algorithm to reduce both the number of rules and the number of attributes involved in the rule set. We have tested our system on real data from the UCI repository. The results of a 10-fold cross validation are compared to C4.5 s and they show an important improvement.Comisión Interministerial de Ciencia y Tecnología TIC99-0351Lenguajes y Sistemas InformáticosComisión Interministerial de Ciencia y Tecnología (CICYT). España2000info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/100151reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésThe International Journal of Computers, Systems and Signal, 1 (1), 73-84.TIC99-0351https://dblp.org/db/journals/ijcss/ijcss1.html#Bajic00info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1001512026-06-17T12:51:07Z
dc.title.none.fl_str_mv Discovering hierarchical decision rules with evolutive algorithms in supervised learning
title Discovering hierarchical decision rules with evolutive algorithms in supervised learning
spellingShingle Discovering hierarchical decision rules with evolutive algorithms in supervised learning
Riquelme Santos, José Cristóbal
Evolutive Algorithms
Supervised Learning
Decision Lists
title_short Discovering hierarchical decision rules with evolutive algorithms in supervised learning
title_full Discovering hierarchical decision rules with evolutive algorithms in supervised learning
title_fullStr Discovering hierarchical decision rules with evolutive algorithms in supervised learning
title_full_unstemmed Discovering hierarchical decision rules with evolutive algorithms in supervised learning
title_sort Discovering hierarchical decision rules with evolutive algorithms in supervised learning
dc.creator.none.fl_str_mv Riquelme Santos, José Cristóbal
Aguilar, Jesús S.
Toro Bonilla, Miguel
author Riquelme Santos, José Cristóbal
author_facet Riquelme Santos, José Cristóbal
Aguilar, Jesús S.
Toro Bonilla, Miguel
author_role author
author2 Aguilar, Jesús S.
Toro Bonilla, Miguel
author2_role author
author
dc.contributor.none.fl_str_mv Lenguajes y Sistemas Informáticos
Comisión Interministerial de Ciencia y Tecnología (CICYT). España
dc.subject.none.fl_str_mv Evolutive Algorithms
Supervised Learning
Decision Lists
topic Evolutive Algorithms
Supervised Learning
Decision Lists
description This paper describes a new approach, HIDER (HIerarchical DEcision Rules), for learning rules in continuous and discrete domains based on evolutive algorithms. The algorithm produces a hierarchical set of rules, that is, the rules must be applied in a speciÞc order. With this policy, the number of rules may be reduced because the rules could be one inside of another. The evolutive algorithm uses both real and binary codiÞcation for the individuals of the population and introduces several new genetic operators. In addition, this paper discusses the capability of learning systems based on an evolutive algorithm to reduce both the number of rules and the number of attributes involved in the rule set. We have tested our system on real data from the UCI repository. The results of a 10-fold cross validation are compared to C4.5 s and they show an important improvement.
publishDate 2000
dc.date.none.fl_str_mv 2000
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/100151
url https://hdl.handle.net/11441/100151
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv The International Journal of Computers, Systems and Signal, 1 (1), 73-84.
TIC99-0351
https://dblp.org/db/journals/ijcss/ijcss1.html#Bajic00
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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