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.
CITATION STYLE
Bello, M., Nápoles, G., Vanhoof, K., & Bello, R. (2019). Prototypes Generation from Multi-label Datasets Based on Granular Computing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11896 LNCS, pp. 142–151). Springer. https://doi.org/10.1007/978-3-030-33904-3_13
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