Detailed results of Multilabel Prototype Generation for Data Reduction in k-Nearest Neighour classification

Detailed experimental results of both the proposed and existing multilabel Prototype Generation methods for Data Reduction in k-Nearest Neighbour classification: 1. General Multilabel PG comparative - General comparison of the proposed methods against the existing proposals in the literature. - Individual results provided for each corpus. - Corresponds to Section 5.1 in the manuscript. 2. Noise robustness - Study of the noise robustness capabilities of the proposed strategies as well as the existing methods. - Individual results provided for each corpus. - Corresponds to Section 5.2 in the manuscript. 3. Imbalanced data - Assessment considering imbalanced data metrics. - Individual results provided for each corpus. - Corresponds to Section 5.3 in the manuscript. 4. Execution time bechmarking - Benchmarking comparative, in terms of execution time, of the assessed methods. - Individual results provided for each iteration considered. - Corresponds to Section 5.4 in the manuscript.