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

Published: 15 September 2022| Version 2 | DOI: 10.17632/rbcnc6jcf3.2
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Description

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.

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Steps to reproduce

All results may be obtained by executing the Github code in the "Related links" section (instructions are provided in the repository).

Institutions

Universitat Pompeu Fabra, Universitat d'Alacant

Categories

Machine Learning, Pattern Recognition

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