@article {792,
	title = {Decomposition-Fusion for Label Distribution Learning},
	journal = {Information Fusion},
	volume = {66},
	year = {2021},
	note = {TIN2017-89517-P},
	month = {02/2021},
	pages = {64-75},
	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.},
	keywords = {Decomposition strategies, Label Distribution Learning, machine learning, One vs. One},
	doi = {https://doi.org/10.1016/j.inffus.2020.08.024},
	author = {Gonzalez, M and Germ{\'a}n Gonz{\'a}lez-Almagro and Triguero, Isaac and J. R. Cano and Garc{\'\i}a, Salvador}
}
