Manuel Gonz�lez
Decomposition-Fusion for Label Distribution Learning
Gonz�lez, Manuel; Gonz�lez-Almagro, Germ�n; Triguero, Isaac; Cano, Jos�-Ram�n; Garc�a, Salvador
Authors
Germ�n Gonz�lez-Almagro
ISAAC TRIGUERO VELAZQUEZ I.TrigueroVelazquez@nottingham.ac.uk
Associate Professor
Jos�-Ram�n Cano
Salvador Garc�a
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.
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 |
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