Prototypes Generation from Multi-label Datasets Based on Granular Computing

  • Marilyn BelloEmail author
  • Gonzalo Nápoles
  • Koen Vanhoof
  • Rafael Bello
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11896)


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.


Multi-label classification Prototype generation Granular Computing Rough Set Theory 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Marilyn Bello
    • 1
    • 2
    Email author
  • Gonzalo Nápoles
    • 2
  • Koen Vanhoof
    • 2
  • Rafael Bello
    • 1
  1. 1.Computer Science DepartmentUniversidad Central de Las VillasSanta ClaraCuba
  2. 2.Faculty of Business EconomicsHasselt UniversityHasseltBelgium

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