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ProLSFEO-LDL: Prototype Selection and LabelSpecific Feature Evolutionary Optimization for Label Distribution Learning
dc.contributor.author | González, Manuel | |
dc.contributor.author | García, Salvador | |
dc.date.accessioned | 2020-06-22T12:11:44Z | |
dc.date.available | 2020-06-22T12:11:44Z | |
dc.date.issued | 2020-04 | |
dc.identifier.citation | González, M., Cano, J. R., & García, S. (2020). ProLSFEO-LDL: Prototype Selection and Label-Specific Feature Evolutionary Optimization for Label Distribution Learning. Applied Sciences, 10(9), 3089. [doi:10.3390/app10093089] | es_ES |
dc.identifier.uri | http://hdl.handle.net/10481/62618 | |
dc.description.abstract | Label Distribution Learning (LDL) is a general learning framework that assigns an instance to a distribution over a set of labels rather than to a single label or multiple labels. Current LDL methods have proven their effectiveness in many real-life machine learning applications. In LDL problems, instance-based algorithms and particularly the adapted version of the k-nearest neighbors method for LDL (AA-kNN) has proven to be very competitive, achieving acceptable results and allowing an explainable model. However, it suffers from several handicaps: it needs large storage requirements, it is not efficient predicting and presents a low tolerance to noise. The purpose of this paper is to mitigate these effects by adding a data reduction stage. The technique devised, called Prototype selection and Label-Specific Feature Evolutionary Optimization for LDL (ProLSFEO-LDL), is a novel method to simultaneously address the prototype selection and the label-specific feature selection pre-processing techniques. Both techniques pose a complex optimization problem with a huge search space. Therefore, we have proposed a search method based on evolutionary algorithms that allows us to obtain a solution to both problems in a reasonable time. The effectiveness of the proposed ProLSFEO-LDL method is verified on several real-world LDL datasets, showing significant improvements in comparison with using raw datasets. | es_ES |
dc.description.sponsorship | This work is supported by the Spanish National Research Project TIN2017-89517-P. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | Atribución 3.0 España | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Label distribution learning | es_ES |
dc.subject | Evolutionary optimization | es_ES |
dc.subject | Protoype selection | es_ES |
dc.subject | Label-specific feature | es_ES |
dc.subject | Machine learning | es_ES |
dc.title | ProLSFEO-LDL: Prototype Selection and LabelSpecific Feature Evolutionary Optimization for Label Distribution Learning | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.doi | 10.3390/app10093089 |