Genetic learning of fuzzy rule bases for multi-label classification using an iterative approach

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Resumen

Multi-label classification problems exist in many real world applications where to each example in the dataset can be assigned a set of target labels. This paper presents a new two-step method for genetic learning of a fuzzy rule base for multi-label classification, called IRL-MLC. The first step uses a genetic algorithm based on an iterative approach to learn a preliminary rule base where the fitness of each rule depends on the degree of firing calculated for the set of labels of each example (positive or negatively) in the dataset. The second step uses a genetic algorithm to tune weights of each fuzzy rule in the preliminary rule base where the fitness of each set of weights is the precision of the multi-label classification. Experiments are conducted on five multi-label datasets, in biology, multimedia and text domains, and the proposed method has been compared with one state-of-art method. Results provide interesting insights into the quality of the discussed novel method.

Idioma originalInglés
Título de la publicación alojada2020 IEEE International Conference on Fuzzy Systems, FUZZ 2020 - Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728169323
DOI
EstadoPublicada - jul. 2020
Evento2020 IEEE International Conference on Fuzzy Systems, FUZZ 2020 - Glasgow, Reino Unido
Duración: 19 jul. 202024 jul. 2020

Serie de la publicación

NombreIEEE International Conference on Fuzzy Systems
Volumen2020-July
ISSN (versión impresa)1098-7584

Conferencia

Conferencia2020 IEEE International Conference on Fuzzy Systems, FUZZ 2020
País/TerritorioReino Unido
CiudadGlasgow
Período19/07/2024/07/20

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© 2020 IEEE.

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