Affiliations: [a] Computer Science Department, Universidad Central de Las Villas, Cuba | [b] Faculty of Business Economics, Hasselt University, Belgium | [c] Department of Cognitive Science and Artificial Intelligence, Tilburg University, The Netherlands
Correspondence:
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Corresponding author: Marilyn Bello, Computer Science Department, Universidad Central de Las Villas, Cuba. E-mail: [email protected].
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 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.
