Abstract
Learning from imbalanced multilabel data is a challenging task that has attracted considerable attention lately. Some resampling algorithms used in traditional classification, such as random undersampling and random oversampling, have been already adapted in order to work with multilabel datasets.
In this paper MLeNN (MultiLabel edited Nearest Neighbor), a heuristic multilabel undersampling algorithm based on the well-known Wilson’s Edited Nearest Neighbor Rule, is proposed. The samples to be removed are heuristically selected, instead of randomly picked. The ability of MLeNN to improve classification results is experimentally tested, and its performance against multilabel random undersampling is analyzed. As will be shown, MLeNN is a competitive multilabel undersampling alternative, able to enhance significantly classification results.
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Charte, F., Rivera, A.J., del Jesus, M.J., Herrera, F. (2014). MLeNN: A First Approach to Heuristic Multilabel Undersampling. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2014. IDEAL 2014. Lecture Notes in Computer Science, vol 8669. Springer, Cham. https://doi.org/10.1007/978-3-319-10840-7_1
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DOI: https://doi.org/10.1007/978-3-319-10840-7_1
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