Decomposition-Fusion for Label Distribution Learning

Gonz�lez, Manuel; Gonz�lez-Almagro, Germ�n; Triguero, Isaac; Cano, Jos�-Ram�n; Garc�a, Salvador

Abstract

Label Distribution Learning (LDL) is a general learning framework that assigns an instance to a distribution over a set of labels rather than to a single label or multiple labels. Current LDL methods have proven their effectiveness in many real-life machine learning applications. However, LDL is a generalization of the classification task and as such it is exposed to the same problems as standard classification algorithms, including class-imbalanced, noise, overlapping or irregularities. The purpose of this paper is to mitigate these effects by using decomposition strategies. The technique devised, called Decomposition-Fusion for LDL (DF-LDL), is based on one of the most renowned strategy in decomposition: the One-vs-One scheme, which we adapt to be able to deal with LDL datasets. In addition, we propose a competent fusion method that allows us to discard non-competent classifiers when their output is probably not of interest. The effectiveness of the proposed DF-LDL method is verified on several real-world LDL datasets on which we have carried out two types of experiments. First, comparing our proposal with the base learners and, second, comparing our proposal with the state-of-the-art LDL algorithms. DF-LDL shows significant improvements in both experiments.

Citation

González, M., González-Almagro, G., Triguero, I., Cano, J., & García, S. (2021). Decomposition-Fusion for Label Distribution Learning. Information Fusion, 66, 64-75. https://doi.org/10.1016/j.inffus.2020.08.024

• Journal Article Type: Article
• Acceptance Date: Aug 29, 2020
• Online Publication Date: Sep 4, 2020
• Publication Date: 2021-02
• Deposit Date: Sep 14, 2020
• Publicly Available Date: Mar 5, 2022
• Journal: Information Fusion
• Print ISSN: 1566-2535
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 66
• Pages: 64-75
• DOI: https://doi.org/10.1016/j.inffus.2020.08.024
• Keywords: Signal Processing; Hardware and Architecture; Software; Information Systems
• Public URL: https://nottingham-repository.worktribe.com/output/4889771
• Publisher URL: https://www.sciencedirect.com/science/article/abs/pii/S1566253520303596