2026-10-13T00:24:27Z https://data.mendeley.com/oai

oai:data.mendeley.com/p6ytjt5rfy.1 2024-04-02T15:36:06Z

Valero-Mas, Jose J. Detailed results of "Insights into imbalance-aware Multilabel Prototype Generation mechanisms for k-Nearest Neighbor classification in noisy scenarios" Mendeley Data Detailed experimental results of the different Prototype Generation strategies for k-Nearest Neighbour classification in multilabel data attending to the particular issues of label-level imbalance and noise: 1. Noise-free scenarios - Study of the considered strategies for addressing label-level imbalance in PG scenarios without induced noise. - Individual results provided for each corpus. - Statistical tests (Friedman and Bonferroni-Dunn with significance level of p < 0.01) to assess the improvement compared to the base multilabel PG strategies - Corresponds to Section 5.1 in the manuscript. 2. Noisy scenarios - Study of the noise robustness capabilities of the proposed strategies. - Individual results provided for each corpus. - Statistical tests (Friedman and Bonferroni-Dunn with significance level of p < 0.01) to assess the improvement compared too the base multilabel PG strategies - Corresponds to Section 5.2 in the manuscript. 3. Results ignoring the Editing stage - Assessment of the relevance of the Editing stage in the general pipeline. - Individual results provided for each corpus. - Corresponds to Section 5.3 in the manuscript. Machine Learning Pattern Recognition Penarrubia, Carlos Castellanos, Francisco J. Gallego, Antonio Javier Calvo-Zaragoza, Jorge Dataset doi:10.17632/p6ytjt5rfy.1 oai:data.mendeley.com/p6ytjt5rfy.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/p6ytjt5rfy 2024-04-02T15:36:06Z