Abstract
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 language | English |
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Title of host publication | Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications |
Editors | Ingela Nyström, Yanio Hernández Heredia, Vladimir Milián Núñez |
Place of Publication | Cham |
Publisher | Springer International Publishing |
Pages | 142-151 |
Number of pages | 10 |
ISBN (Print) | 978-3-030-33904-3 |
Publication status | Published - 2019 |
Externally published | Yes |
Event | 24th Iberoamerican Congress CIARP 2019 - Duration: 16 Oct 2019 → … |
Conference
Conference | 24th Iberoamerican Congress CIARP 2019 |
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Abbreviated title | CIARP |
Period | 16/10/19 → … |