GPPE: a method to generate ad-hoc feature extractors for prediction in financial domains

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dc.contributor.author Estébanez Tascón, César
dc.contributor.author Valls, José M.
dc.contributor.author Aler, Ricardo
dc.date.accessioned 2009-12-02T08:58:16Z
dc.date.available 2009-12-02T08:58:16Z
dc.date.issued 2008
dc.identifier.bibliographicCitation Applied Intelligence Journal 2008, vol. 29, n. 2, p. 174-185
dc.identifier.issn 0924-669X (Print)
dc.identifier.issn 1573-7497 (Online)
dc.identifier.uri http://hdl.handle.net/10016/5897
dc.description.abstract When dealing with classification and regression problems, there is a strong need for high-quality attributes. This is a capital issue not only in financial problems, but in many Data Mining domains. Constructive Induction methods help to overcome this problem by mapping the original representation into a new one, where prediction becomes easier. In this work we present GPPE: a GP-based method that projects data from an original data space into another one where data approaches linear behavior (linear separability or linear regression). Also, GPPE is able to reduce the dimensionality of the problem by recombining related attributes and discarding irrelevant ones. We have applied GPPE to two financial domains: Bankruptcy prediction and IPO Underpricing prediction. In both cases GPPE automatically generated a new data representation that obtained competitive prediction rates and drastically reduced the dimensionality of the problem.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Springer
dc.rights © Springer
dc.subject.other Genetic programming
dc.subject.other Projections
dc.subject.other Attribute construction
dc.subject.other Dimensionality reduction
dc.title GPPE: a method to generate ad-hoc feature extractors for prediction in financial domains
dc.type article
dc.type.review PeerReviewed
dc.description.status Publicado
dc.relation.publisherversion http://dx.doi.org/10.1007/s10489-007-0048-0
dc.subject.eciencia Informática
dc.identifier.doi 10.1007/s10489-007-0048-0
dc.rights.accessRights openAccess
dc.identifier.publicationfirstpage 174
dc.identifier.publicationissue 2
dc.identifier.publicationlastpage 185
dc.identifier.publicationtitle Applied Intelligence Journal
dc.identifier.publicationvolume 29
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