dc.creator | Aguilar Ruiz, Jesús Salvador | es |
dc.creator | Giráldez Rojo, Raúl | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.date.accessioned | 2016-07-07T09:22:33Z | |
dc.date.available | 2016-07-07T09:22:33Z | |
dc.date.issued | 2007 | |
dc.identifier.citation | Aguilar Ruiz, J.S., Giráldez Rojo, R. y Riquelme Santos, J.C. (2007). Natural Encoding for Evolutionary Supervised Learning. IEEE Transactions on Evolutionary Computation, 11 (4), 466-479. | |
dc.identifier.issn | 1089-778X | es |
dc.identifier.uri | http://hdl.handle.net/11441/43301 | |
dc.description.abstract | Some of the most influential factors in the quality of
the solutions found by an evolutionary algorithm (EA) are a correct
coding of the search space and an appropriate evaluation function
of the potential solutions. EAs are often used to learn decision
rules from datasets, which are encoded as individuals in the genetic
population. In this paper, the coding of the search space for
the obtaining of those decision rules is approached, i.e., the representation
of the individuals of the genetic population and also
the design of specific genetic operators. Our approach, called “natural
coding,” uses one gene per feature in the dataset (continuous
or discrete). The examples from the datasets are also encoded into
the search space, where the genetic population evolves, and therefore
the evaluation process is improved substantially. Genetic operators
for the natural coding are formally defined as algebraic
expressions.
Experiments with several datasets from the University of
California at Irvine (UCI) machine learning repository show that
as the genetic operators are better guided through the search
space, the number of rules decreases considerably while maintaining
the accuracy, similar to that of hybrid coding, which joins
the well-known binary and real representations to encode discrete
and continuous attributes, respectively. The computational cost
associated with the natural coding is also reduced with regard to
the hybrid representation.
Our algorithm, HIDER*, has been statistically tested against
C4.5 and C4.5 Rules, and performed well. The knowledge models
obtained are simpler, with very few decision rules, and therefore
easier to understand, which is an advantage in many domains.
The experiments with high-dimensional datasets showed the same
good behavior, maintaining the quality of the knowledge model
with respect to prediction accuracy. | es |
dc.description.sponsorship | CICYT TIN2004-00159 | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | IEEE | es |
dc.relation.ispartof | IEEE Transactions on Evolutionary Computation, 11 (4), 466-479. | |
dc.relation.ispartof | IEEE Transactions on Evolutionary Computation, 11 (4), 466-479. | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Decision rules | es |
dc.subject | evolutionary encoding | es |
dc.subject | supervised learning | es |
dc.title | Natural Encoding for Evolutionary Supervised Learning | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2004-00159 | es |
dc.identifier.doi | http://dx.doi.org/10.1109/TEVC.2006.883466 | es |
idus.format.extent | 14 | es |
dc.journaltitle | IEEE Transactions on Evolutionary Computation | es |
dc.publication.volumen | 11 | es |
dc.publication.issue | 4 | es |
dc.publication.initialPage | 466 | es |
dc.publication.endPage | 479 | es |
dc.identifier.idus | https://idus.us.es/xmlui/handle/11441/43301 | |
dc.contributor.funder | Comisión Interministerial de Ciencia y Tecnología (CICYT). España | |