Prototypes Generation from Multi-label Datasets Based on Granular Computing

Marilyn Bello*, Gonzalo Nápoles, Koen Vanhoof, Rafael Bello

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Data reduction techniques play a key role in instance-based classification to lower the amount of data to be processed. Prototype generation aims to obtain a reduced training set in order to obtain accurate results with less effort. This translates into a significant reduction in both algorithms' spatial and temporal burden. This issue is particularly relevant in multi-label classification, which is a generalization of multiclass classification that allows objects to belong to several classes simultaneously. Although this field is quite active in terms of learning algorithms, there is a lack of prototype generation methods. In this research, we propose three prototype generation methods from multi-label datasets based on Granular Computing. The experimental results show that these methods reduce the number of examples into a set of prototypes without affecting the overall performance.
Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
EditorsIngela Nyström, Yanio Hernández Heredia, Vladimir Milián Núñez
Place of PublicationCham
PublisherSpringer International Publishing
Number of pages10
ISBN (Print)978-3-030-33904-3
Publication statusPublished - 2019
Externally publishedYes
Event24th Iberoamerican Congress CIARP 2019 -
Duration: 16 Oct 2019 → …


Conference24th Iberoamerican Congress CIARP 2019
Abbreviated titleCIARP
Period16/10/19 → …


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