On the generation of multi-label prototypes

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

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)


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 data reduction methods. In this paper, we propose several prototype generation methods from multi-label datasets based on Granular Computing. The simulations show that these methods significantly reduce the number of examples to a set of prototypes without significantly affecting classifiers’ performance.
Original languageEnglish
Pages (from-to)167-183
Number of pages17
JournalIntelligent Data Analysis
Publication statusPublished - 2020


  • Multi-label classification
  • prototype generation
  • granular computing


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