Decomposition-Fusion for Label Distribution Learning
| Autor: | José Ramón Cano, Isaac Triguero, Salvador García, Germán González-Almagro, Manuel Fernández González |
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| Rok vydání: | 2021 |
| Předmět: |
Scheme (programming language)
Computer science business.industry Generalization 020206 networking & telecommunications 02 engineering and technology Base (topology) Machine learning computer.software_genre Task (project management) Set (abstract data type) Statistical classification TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES Hardware and Architecture Signal Processing 0202 electrical engineering electronic engineering information engineering Decomposition (computer science) 020201 artificial intelligence & image processing Artificial intelligence Noise (video) business computer Software Information Systems computer.programming_language |
| Zdroj: | Information Fusion. 66:64-75 |
| ISSN: | 1566-2535 |
| DOI: | 10.1016/j.inffus.2020.08.024 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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