Predicting Problem Difficulty for Genetic Programming Applied to Data Classification
| hal.structure.identifier | Instituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana] | |
| dc.contributor.author | TRUJILLO, Leonardo | |
| hal.structure.identifier | Instituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana] | |
| dc.contributor.author | MARTINEZ, Yuliana | |
| hal.structure.identifier | School of Computer Science and Electronic Engineering | |
| dc.contributor.author | GALVAN-LOPEZ, Edgar | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| hal.structure.identifier | Advanced Learning Evolutionary Algorithms [ALEA] | |
| dc.contributor.author | LEGRAND, Pierrick | |
| dc.contributor.editor | Natalio KrasnogorUniversity of Nottingham, UK | |
| dc.date.issued | 2011 | |
| dc.date.conference | 2011-07-12 | |
| dc.description.abstractEn | During the development of applied systems, an important problem that must be addressed is that of choosing the correct tools for a given domain or scenario. This general task has been addressed by the genetic programming (GP) community by attempting to determine the intrinsic difficulty that a problem poses for a GP search. This paper presents an approach to predict the performance of GP applied to data classification, one of themost common problems in computer science. The novelty of the proposal is to extract statistical descriptors and complexity descriptors of the problem data, and from these estimate the expected performance of a GP classifier. We derive two types of predictive models: linear regression models and symbolic regression models evolved with GP. The experimental results show that both approaches provide good estimates of classifier performance, using synthetic and real-world problems for validation. In conclusion, this paper shows that it is possible to accurately predict the expected performance of a GP classifier using a set of descriptors that characterize the problem data. | |
| dc.language.iso | en | |
| dc.publisher | ACM New York, NY, USA ©2011 | |
| dc.title.en | Predicting Problem Difficulty for Genetic Programming Applied to Data Classification | |
| dc.type | Communication dans un congrès | |
| dc.identifier.doi | 10.1145/2001576.2001759 | |
| dc.subject.hal | Informatique [cs]/Traitement du signal et de l'image | |
| dc.subject.hal | Sciences de l'ingénieur [physics]/Traitement du signal et de l'image | |
| dc.subject.hal | Informatique [cs]/Intelligence artificielle [cs.AI] | |
| dc.subject.hal | Mathématiques [math]/Statistiques [math.ST] | |
| dc.subject.hal | Statistiques [stat]/Théorie [stat.TH] | |
| bordeaux.page | 1355-1362 | |
| bordeaux.conference.title | Gecco 2011 | |
| bordeaux.country | IE | |
| bordeaux.conference.city | Dublin | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-00643358 | |
| hal.version | 1 | |
| hal.invited | non | |
| hal.proceedings | oui | |
| hal.conference.end | 2011-07-16 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-00643358v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2011&rft.spage=1355-1362&rft.epage=1355-1362&rft.au=TRUJILLO,%20Leonardo&MARTINEZ,%20Yuliana&GALVAN-LOPEZ,%20Edgar&LEGRAND,%20Pierrick&rft.genre=unknown |
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