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Title: | Prototypes Generation from Multi-label Datasets based on Granular Computing | Authors: | BELLO GARCIA, Marilyn NAPOLES RUIZ, Gonzalo VANHOOF, Koen Bello, Rafael |
Issue Date: | 2019 | Publisher: | Springer | Source: | Nyström, Ingela; Hernández Heredia, Yanio; Milián Núñez, Vladimir (Ed.).Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications 24th Iberoamerican Congress, CIARP 2019, Havana, Cuba, October 28-31, 2019, Proceedings, p. 142-151 | Series/Report: | Lecture Notes in Computer Science | Series/Report no.: | 11896 | 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. | Keywords: | Multi-label classification;Prototype generation;Granular Computing;Rough Set Theory | Document URI: | http://hdl.handle.net/1942/30506 | ISBN: | 9783030339036 | ISSN: | 0302-9743 | DOI: | 10.1007/978-3-030-33904-3_13 | ISI #: | 000582428400013 | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2022 vabb 2023 |
Appears in Collections: | Research publications |
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