Show simple item record

dc.creatorSanz Delgado, José Antonioes_ES
dc.creatorBernardo, Darioes_ES
dc.creatorHerrera, Franciscoes_ES
dc.creatorBustince Sola, Humbertoes_ES
dc.creatorHagras, Hanies_ES
dc.date.accessioned2015-07-27T10:07:19Z
dc.date.available2015-07-27T10:07:19Z
dc.date.issued2014
dc.identifier.citationJ. A. Sanz, D. Bernardo, F. Herrera, H. Bustince and H. Hagras, "A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data," in IEEE Transactions on Fuzzy Systems, vol. 23, no. 4, pp. 973-990, Aug. 2015. doi: 10.1109/TFUZZ.2014.2336263en
dc.identifier.issn1063-6706
dc.identifier.urihttps://hdl.handle.net/2454/17689
dc.description.abstractThe current financial crisis has stressed the need of obtaining more accurate prediction models in order to decrease the risk when investing money on economic opportunities. In addition, the transparency of the process followed to make the decisions in financial applications is becoming an important issue. Furthermore, there is a need to handle the real-world imbalanced financial data sets without using sampling techniques which might introduce noise in the used data. In this paper, we present a compact evolutionary interval-valued fuzzy rule-based classification system, which is based on IVTURSFARC-HD (Interval-Valued fuzzy rule-based classification system with TUning and Rule Selection) [22]), for the modeling and prediction of real-world financial applications. This proposed system allows obtaining good predictions accuracies using a small set of short fuzzy rules implying a high degree of interpretability of the generated linguistic model. Furthermore, the proposed system deals with the financial imbalanced datasets with no need for any preprocessing or sampling method and thus avoiding the accidental introduction of noise in the data used in the learning process. The system is also provided with a mechanism to handle examples that are not covered by any fuzzy rule in the generated rule base. To test the quality of our proposal, we will present an experimental study including eleven real-world financial datasets. We will show that the proposed system outperforms the original C4.5 decision tree, type-1 and interval-valued fuzzy counterparts which use the SMOTE sampling technique to preprocess data and the original FURIA, which is a fuzzy approximative classifier. Furthermore, the proposed method enhances the results achieved by the cost sensitive C4.5 and it gives competitive results when compared with FURIA using SMOTE, while our proposal avoids pre-processing techniques and it provides interpretable models that allow obtaining more accurate results.en
dc.description.sponsorshipThis work was supported in part by the Spanish Ministry of Science and Technology under Project TIN2011-28488 and Project TIN2013-40765.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherIEEEen
dc.relation.ispartofIEEE Transactions on Fuzzy Systemsen
dc.rights© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.subjectFinancial applicationsen
dc.subjectInterval-valued fuzzy setsen
dc.subjectInterval-valued fuzzy rule-based classification systemsen
dc.subjectEvolutionary algorithmsen
dc.titleA compact evolutionary interval-valued fuzzy rule-based classification system for the modeling and prediction of real-world financial applications with imbalanced dataen
dc.typeArtículo / Artikuluaes
dc.typeinfo:eu-repo/semantics/articleen
dc.contributor.departmentUniversidad Pública de Navarra. Departamento de Automática y Computaciónes_ES
dc.contributor.departmentNafarroako Unibertsitate Publikoa. Automatika eta Konputazioa Sailaeu
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.identifier.doi10.1109/TFUZZ.2014.2336263
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/6PN/TIN2011-28488en
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/6PN/TIN2013-40765en
dc.relation.publisherversionhttps://dx.doi.org/10.1109/TFUZZ.2014.2336263
dc.type.versionVersión aceptada / Onetsi den bertsioaes
dc.type.versioninfo:eu-repo/semantics/acceptedVersionen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record