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dc.contributor.authorQuintián, Héctor
dc.contributor.authorCorchado, Emilio
dc.date.accessioned2020-08-10T10:44:59Z
dc.date.available2020-08-10T10:44:59Z
dc.date.issued2020-06-11
dc.identifier.citationH. Quintián and E. Corchado, "A Novel Ensemble Beta-Scale Invariant Map Algorithm," in IEEE Access, vol. 8, pp. 108857-108884, 2020, doi: 10.1109/ACCESS.2020.3001690.es_ES
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/2183/26122
dc.description.abstract[Abstract]: This research presents a novel topology preserving map (TPM) called Weighted Voting Supervision -Beta-Scale Invariant Map (WeVoS-Beta-SIM), based on the application of the Weighted Voting Supervision (WeVoS) meta-algorithm to a novel family of learning rules called Beta-Scale Invariant Map (Beta-SIM). The aim of the novel TPM presented is to improve the original models (SIM and Beta-SIM) in terms of stability and topology preservation and at the same time to preserve their original features, especially in the case of radial datasets, where they all are designed to perform their best. These scale invariant TPM have been proved with very satisfactory results in previous researches. This is done by generating accurate topology maps in an effectively and efficiently way. WeVoS meta-algorithm is based on the training of an ensemble of networks and the combination of them to obtain a single one that includes the best features of each one of the networks in the ensemble. WeVoS-Beta-SIM is thoroughly analyzed and successfully demonstrated in this study over 14 diverse real benchmark datasets with diverse number of samples and features, using three different well-known quality measures. In order to present a complete study of its capabilities, results are compared with other topology preserving models such as Self Organizing Maps, Scale Invariant Map, Maximum Likelihood Hebbian Learning-SIM, Visualization Induced SOM, Growing Neural Gas and Beta- Scale Invariant Map. The results obtained confirm that the novel algorithm improves the quality of the single Beta-SIM algorithm in terms of topology preservation and stability without losing performance (where this algorithm has proved to overcome other well-known algorithms). This improvement is more remarkable when complexity of the datasets increases, in terms of number of features and samples and especially in the case of radial datasets improving the Topographic Error.es_ES
dc.language.isoenges_ES
dc.publisherInstitute of Electrical and Electronics Engineers Inc.es_ES
dc.relation.urihttps://doi.org/10.1109/ACCESS.2020.3001690es_ES
dc.rightsCreative Commons License Attribution 4.0 International (CC BY 4.0)es_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/es/*
dc.subjectEnsembleses_ES
dc.subjectTopology preserving mappinges_ES
dc.subjectQuality measureses_ES
dc.subjectSOMes_ES
dc.subjectSIMes_ES
dc.subjectViSOMes_ES
dc.subjectMLHL-SIMes_ES
dc.subjectGNGes_ES
dc.subjectBeta-SIMes_ES
dc.subjectWeVoSes_ES
dc.titleA novel ensemble Beta-scale invariant map algorithmes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleIEEE Accesses_ES
UDC.volume8es_ES
UDC.startPage108857es_ES
UDC.endPage108884es_ES
dc.identifier.doihttps://doi.org/10.1109/ACCESS.2020.3001690


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