Unsupervised fuzzy measure learning for classifier ensembles from coalitions performance
| dc.contributor.author | Uriz Martín, Mikel Xabier | |
| dc.contributor.author | Paternain Dallo, Daniel | |
| dc.contributor.author | Domínguez Catena, Iris | |
| dc.contributor.author | Bustince Sola, Humberto | |
| dc.contributor.author | Galar Idoate, Mikel | |
| dc.contributor.department | Institute of Smart Cities - ISC | en |
| dc.contributor.funder | Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, PJUPNA13 | es |
| dc.date.accessioned | 2020-07-03T12:54:09Z | |
| dc.date.available | 2020-07-03T12:54:09Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | In Machine Learning an ensemble refers to the combination of several classifiers with the objective of improving the performance of every one of its counterparts. To design an ensemble two main aspects must be considered: how to create a diverse set of classifiers and how to combine their outputs. This work focuses on the latter task. More specifically, we focus on the usage of aggregation functions based on fuzzy measures, such as the Sugeno and Choquet integrals, since they allow to model the coalitions and interactions among the members of the ensemble. In this scenario the challenge is how to construct a fuzzy measure that models the relations among the members of the ensemble. We focus on unsupervised methods for fuzzy measure construction, review existing alternatives and categorize them depending on their features. Furthermore, we intend to address the weaknesses of previous alternatives by proposing a new construction method that obtains the fuzzy measure directly evaluating the performance of each possible subset of classifiers, which can be efficiently computed. To test the usefulness of the proposed fuzzy measure, we focus on the application of ensembles for imbalanced datasets. We consider a set of 66 imbalanced datasets and develop a complete experimental study comparing the reviewed methods and our proposal. | en |
| dc.description.sponsorship | This work was supported in part by the Spanish Ministry of Science and Technology (AEI/FEDER, UE) under Project TIN2016-77356-P, and in part by the Public University of Navarra under Project PJUPNA13. | en |
| dc.format.extent | 18 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | M. Uriz, D. Paternain, I. Dominguez-Catena, H. Bustince and M. Galar, 'Unsupervised Fuzzy Measure Learning for Classifier Ensembles From Coalitions Performance,' in IEEE Access, vol. 8, pp. 52288-52305, 2020, doi: 10.1109/ACCESS.2020.2980949. | en |
| dc.identifier.doi | 10.1109/ACCESS.2020.2980949 | |
| dc.identifier.issn | 2169-3536 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/37293 | |
| dc.language.iso | eng | en |
| dc.publisher | IEEE | en |
| dc.relation.ispartof | IEEE Access, 2020, 8, 52288-52305 | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/ES/1PE/TIN2016-77356-P/ | |
| dc.relation.publisherversion | https://doi.org/10.1109/ACCESS.2020.2980949 | |
| dc.rights | This work is licensed under a Creative Commons Attribution 4.0 License. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.subject | Fuzzy measures | en |
| dc.subject | Choquet integral | en |
| dc.subject | Aggregation | en |
| dc.subject | Ensembles | en |
| dc.subject | Classification | en |
| dc.title | Unsupervised fuzzy measure learning for classifier ensembles from coalitions performance | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | |
| dspace.entity.type | Publication | |
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