Publication: A supervised fuzzy measure learning algorithm for combining classifiers
| dc.contributor.author | Uriz Martín, Mikel Xabier | |
| dc.contributor.author | Paternain Dallo, Daniel | |
| 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 | es |
| dc.date.accessioned | 2023-05-11T06:18:27Z | |
| dc.date.issued | 2023 | |
| dc.date.updated | 2023-05-11T06:08:52Z | |
| dc.description.abstract | Fuzzy measure-based aggregations allow taking interactions among coalitions of the input sources into account. Their main drawback when applying them in real-world problems, such as combining classifier ensembles, is how to define the fuzzy measure that governs the aggregation and specifies the interactions. However, their usage for combining classifiers has shown its advantage. The learning of the fuzzy measure can be done either in a supervised or unsupervised manner. This paper focuses on supervised approaches. Existing supervised approaches are designed to minimize the mean squared error cost function, even for classification problems. We propose a new fuzzy measure learning algorithm for combining classifiers that can optimize any cost function. To do so, advancements from deep learning frameworks are considered such as automatic gradient computation. Therefore, a gradient-based method is presented together with three new update policies that are required to preserve the monotonicity constraints of the fuzzy measures. The usefulness of the proposal and the optimization of cross-entropy cost are shown in an extensive experimental study with 58 datasets corresponding to both binary and multi-class classification problems. In this framework, the proposed method is compared with other state-of-the-art methods for fuzzy measure learning. | en |
| dc.description.sponsorship | Mikel Uriz has been supported by the CDTI and the Spanish Ministry of Science and Innovation under Neotec 2021 (SNEO-20211147). This work was also supported by the Spanish Ministry of Science and Innovation under project PID2019-108392 GB-I00 (AEI/10.13039/501100011033) and by the Public University of Navarre under project PJUPNA25-2022. | en |
| dc.embargo.lift | 2025-04-01 | |
| dc.embargo.terms | 2025-04-01 | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Uriz, M., Paternain, D., Bustince, H., Galar, M. (2023) A supervised fuzzy measure learning algorithm for combining classifiers. Information Sciences, 622, 490-511. https://doi.org/10.1016/j.ins.2022.11.161. | en |
| dc.identifier.doi | 10.1016/j.ins.2022.11.161 | |
| dc.identifier.issn | 0020-0255 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/45261 | |
| dc.language.iso | eng | en |
| dc.publisher | Elsevier | en |
| dc.relation.ispartof | Information Sciences, 622 (2023) 490-511 | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108392 GB-I00/ES/ | en |
| dc.relation.publisherversion | https://doi.org/10.1016/j.ins.2022.11.161 | |
| dc.rights | © 2022 Elsevier Inc. This manuscript version is made available under the CC-BY-NC-ND 4.0. | en |
| dc.rights.accessRights | Acceso embargado / Sarbidea bahitua dago | es |
| dc.rights.accessRights | info:eu-repo/semantics/embargoedAccess | en |
| dc.subject | Aggregation | en |
| dc.subject | Choquet integral | en |
| dc.subject | Classification | en |
| dc.subject | Ensembles | en |
| dc.subject | Fuzzy measures | en |
| dc.title | A supervised fuzzy measure learning algorithm for combining classifiers | 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 | |
| relation.isAuthorOfPublication | a3183ffc-8d9f-4a63-8b70-e3e733e23f6b | |
| relation.isAuthorOfPublication | ca16c024-51e4-4f8f-b457-dc5307be32d9 | |
| relation.isAuthorOfPublication | 1bdd7a0e-704f-48e5-8d27-4486444f82c9 | |
| relation.isAuthorOfPublication | 44c7a308-9c21-49ef-aa03-b45c2c5a06fd | |
| relation.isAuthorOfPublication.latestForDiscovery | a3183ffc-8d9f-4a63-8b70-e3e733e23f6b |