Do we still need fuzzy classifiers for small data in the era of big data?
| dc.contributor.author | Elkano Ilintxeta, Mikel | |
| 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 | 2020-05-21T09:23:38Z | |
| dc.date.available | 2020-10-10T23:00:13Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | The Era of Big Data has forced researchers to explore new distributed solutions for building fuzzy classifiers, which often introduce approximation errors or make strong assumptions to reduce computational and memory requirements. As a result, Big Data classifiers might be expected to be inferior to those designed for standard classification tasks (Small Data) in terms of accuracy and model complexity. To our knowledge, however, there is no empirical evidence to confirm such a conjecture yet. Here, we investigate the extent to which state-of-the-art fuzzy classifiers for Big Data sacrifice performance in favor of scalability. To this end, we carry out an empirical study that compares these classifiers with some of the best performing algorithms for Small Data. Assuming the latter were generally designed for maximizing performance without considering scalability issues, the results of this study provide some intuition around the tradeoff between performance and scalability achieved by current Big Data solutions. Our findings show that, although slightly inferior, Big Data classifiers are gradually catching up with state-of-the-art classifiers for Small data, suggesting that a unified learning algorithm for Big and Small Data might be possible. | en |
| dc.description.sponsorship | This work has been supported by the Spanish Ministry of Economy and Competitiveness under the project TIN2016-77356-P (MINECO, AEI/FEDER, UE) and by the Public University of Navarra under the project PJUPNA13. | en |
| dc.embargo.lift | 2020-10-10 | |
| dc.embargo.terms | 2020-10-10 | |
| dc.format.extent | 7 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | M. Elkano, H. Bustince and M. Galar, 'Do we still need fuzzy classifiers for Small Data in the Era of Big Data?,' 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA, 2019, pp. 1-6, doi: 10.1109/FUZZ-IEEE.2019.8858943. | en |
| dc.identifier.doi | 10.1109/FUZZ-IEEE.2019.8858943 | |
| dc.identifier.isbn | 978-1-5386-1728-1 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/36949 | |
| dc.language.iso | eng | en |
| dc.publisher | IEEE | en |
| dc.relation.ispartof | 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE): 23-26 June, New Orleans, Louisiana, USA, pp. 1-6 | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/ES/1PE/TIN2016-77356-P/ | |
| dc.relation.publisherversion | https://doi.org/10.1109/FUZZ-IEEE.2019.8858943 | |
| dc.rights | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other work. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.subject | Big Data | en |
| dc.subject | Fuzzy sets | en |
| dc.subject | Scalability | en |
| dc.subject | Computational modeling | en |
| dc.subject | Data models | en |
| dc.subject | Complexity theory | en |
| dc.subject | Approximation algorithms | en |
| dc.title | Do we still need fuzzy classifiers for small data in the era of big data? | en |
| dc.type | info:eu-repo/semantics/conferenceObject | |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | |
| dspace.entity.type | Publication | |
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