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Instance selection for multi-label learning based on a scalable evolutionary algorithm
dc.contributor.author | Romero-del-Castillo, Juan A. | |
dc.contributor.author | Ortiz-Boyer, Domingo | |
dc.contributor.author | García Pedrajas, Nicolás | |
dc.date.accessioned | 2024-01-24T17:19:20Z | |
dc.date.available | 2024-01-24T17:19:20Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 2375-9232 | |
dc.identifier.uri | http://hdl.handle.net/10396/26741 | |
dc.description | Embargado hasta 01/01/2100 | |
dc.description.abstract | Multi-label classification has recently attracted greater research interest as a data mining task. Many current ap- plications in data mining address problems having instances that belong to more than one class, which requires the development of new efficient methods. Instance-based classification models, such as the k-nearest neighbor rule, are among the highest performing methods on any classification task and have also been successfully applied to multi-label problems. Despite their simplicity, they achieve comparable performance compared to considerably more com- plex methods. One of the challenges associated with instance- based classification models is their requirement for storing all training instances in memory. To ameliorate this problem, instance selection methods have been proposed. However, their application to multi-label problems is problematic because the adaptation of most of their concepts to multi-label problems is difficult. In this paper, we propose a scalable evolutionary algorithm for instance selection for multi-label problems. As our evolutionary algorithm is solely based on the performance of a subset of selected instances, it is able to handle multi-label datasets. On a set of 12 real-world problems, our approach performs comparably to methods that use all instances while achieving a large reduction in the size of the training set. Index Terms—Instance selection; evolutionary algorithms; classification models; multi-label learning; multi-Label kNN. | es_ES |
dc.format.mimetype | application/pdf | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | IEEE | es_ES |
dc.rights | https://creativecommons.org/licenses/by-nc-nd/4.0/ | es_ES |
dc.source | J. Del Castillo, D. Ortiz-Boyer and N. Garcia-Pedrajas, "Instance selection for multi-label learning based on a scalable evolutionary algorithm," in 2021 International Conference on Data Mining Workshops (ICDMW), Auckland, New Zealand, 2021 pp. 843-851. doi: 10.1109/ICDMW53433.2021.00108 | es_ES |
dc.subject | Instance selection | es_ES |
dc.subject | Evolutionary algorithms | es_ES |
dc.subject | Classification models | es_ES |
dc.subject | Multi-label learning | es_ES |
dc.subject | Multi-Label kNN | es_ES |
dc.title | Instance selection for multi-label learning based on a scalable evolutionary algorithm | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.relation.publisherversion | http://dx.doi.org/10.1109/ICDMW53433.2021.00108 | es_ES |
dc.relation.projectID | Gobierno de España. PID2019-109481GB-I00 | es_ES |
dc.relation.projectID | Junta de Andalucía. UCO-1264182 | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/embargoedAccess | es_ES |
dc.date.embargoEndDate | info:eu-repo/date/embargoEnd/2100-01-01 |