Prototypes Generation from Multi-label Datasets Based on Granular Computing

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.