Publication: Improving the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets and genetic amplitude tuning
| dc.contributor.author | Sanz Delgado, José Antonio | |
| dc.contributor.author | Fernández, Alberto | |
| dc.contributor.author | Bustince Sola, Humberto | |
| dc.contributor.author | Herrera, Francisco | |
| dc.contributor.department | Automática y Computación | es_ES |
| dc.contributor.department | Automatika eta Konputazioa | eu |
| dc.date.accessioned | 2015-07-27T09:43:12Z | |
| dc.date.available | 2015-07-27T09:43:12Z | |
| dc.date.issued | 2010 | |
| dc.description.abstract | Among the computational intelligence techniques employed to solve classification problems, Fuzzy Rule-Based Classification Systems (FRBCSs) are a popular tool because of their interpretable models based on linguistic variables, which are easier to understand for the experts or end-users. The aim of this paper is to enhance the performance of FRBCSs by extending the Knowledge Base with the application of the concept of Interval-Valued Fuzzy Sets (IVFSs). We consider a post-processing genetic tuning step that adjusts the amplitude of the upper bound of the IVFS to contextualize the fuzzy partitions and to obtain a most accurate solution to the problem. We analyze the goodness of this approach using two basic and well-known fuzzy rule learning algorithms, the Chi et al.’s method and the fuzzy hybrid genetics-based machine learning algorithm. We show the improvement achieved by this model through an extensive empirical study with a large collection of data-sets. | en |
| dc.description.sponsorship | This work has been supported by the Spanish Ministry of Science and Technology under projects TIN2008-06681-C06-01 and TIN2007-65981. | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | 10.1016/j.ins.2010.06.018 | |
| dc.identifier.issn | 0020-0255 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/17683 | |
| dc.language.iso | eng | en |
| dc.publisher | Elsevier | en |
| dc.relation.ispartof | Information Sciences 180 (2010) 3674–3685 | en |
| dc.relation.publisherversion | https://dx.doi.org/10.1016/j.ins.2010.06.018 | |
| dc.rights | © 2010 Elsevier Inc. The manuscript version is made available under the CC BY-NC-ND 4.0 license | en |
| dc.rights.accessRights | Acceso abierto / Sarbide irekia | es |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | en |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Fuzzy rule-based classification systems | en |
| dc.subject | Interval-valued fuzzy sets | en |
| dc.subject | Tuning | en |
| dc.subject | Genetic algorithms | en |
| dc.title | Improving the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets and genetic amplitude tuning | en |
| dc.type | Artículo / Artikulua | es |
| dc.type | info:eu-repo/semantics/article | en |
| dc.type.version | Versión aceptada / Onetsi den bertsioa | es |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | en |
| dspace.entity.type | Publication | |
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