Title | Decomposition-Fusion for Label Distribution Learning |
Publication Type | Journal Article |
Year of Publication | 2021 |
Authors | Gonzalez, M, González-Almagro Germán, Triguero Isaac, Cano J. R., and García Salvador |
Journal | Information Fusion |
Volume | 66 |
Pagination | 64-75 |
Date Published | 02/2021 |
Keywords | Decomposition strategies, Label Distribution Learning, machine learning, One vs. One |
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. |
Notes | TIN2017-89517-P |
DOI | 10.1016/j.inffus.2020.08.024 |
Decomposition-Fusion for Label Distribution Learning
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