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Multi-Graph Multi-Label Learning Based on Entropy

Recently, Multi-Graph Learning was proposed as the extension of Multi-Instance Learning and has achieved some successes. However, to the best of our knowledge, currently, there is no study working on Multi-Graph Multi-Label Learning, where each object is represented as a bag containing a number of g...

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Detalles Bibliográficos
Autores principales: Zhu, Zixuan, Zhao, Yuhai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512760/
https://www.ncbi.nlm.nih.gov/pubmed/33265336
http://dx.doi.org/10.3390/e20040245
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author Zhu, Zixuan
Zhao, Yuhai
author_facet Zhu, Zixuan
Zhao, Yuhai
author_sort Zhu, Zixuan
collection PubMed
description Recently, Multi-Graph Learning was proposed as the extension of Multi-Instance Learning and has achieved some successes. However, to the best of our knowledge, currently, there is no study working on Multi-Graph Multi-Label Learning, where each object is represented as a bag containing a number of graphs and each bag is marked with multiple class labels. It is an interesting problem existing in many applications, such as image classification, medicinal analysis and so on. In this paper, we propose an innovate algorithm to address the problem. Firstly, it uses more precise structures, multiple Graphs, instead of Instances to represent an image so that the classification accuracy could be improved. Then, it uses multiple labels as the output to eliminate the semantic ambiguity of the image. Furthermore, it calculates the entropy to mine the informative subgraphs instead of just mining the frequent subgraphs, which enables selecting the more accurate features for the classification. Lastly, since the current algorithms cannot directly deal with graph-structures, we degenerate the Multi-Graph Multi-Label Learning into the Multi-Instance Multi-Label Learning in order to solve it by MIML-ELM (Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine). The performance study shows that our algorithm outperforms the competitors in terms of both effectiveness and efficiency.
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spelling pubmed-75127602020-11-09 Multi-Graph Multi-Label Learning Based on Entropy Zhu, Zixuan Zhao, Yuhai Entropy (Basel) Article Recently, Multi-Graph Learning was proposed as the extension of Multi-Instance Learning and has achieved some successes. However, to the best of our knowledge, currently, there is no study working on Multi-Graph Multi-Label Learning, where each object is represented as a bag containing a number of graphs and each bag is marked with multiple class labels. It is an interesting problem existing in many applications, such as image classification, medicinal analysis and so on. In this paper, we propose an innovate algorithm to address the problem. Firstly, it uses more precise structures, multiple Graphs, instead of Instances to represent an image so that the classification accuracy could be improved. Then, it uses multiple labels as the output to eliminate the semantic ambiguity of the image. Furthermore, it calculates the entropy to mine the informative subgraphs instead of just mining the frequent subgraphs, which enables selecting the more accurate features for the classification. Lastly, since the current algorithms cannot directly deal with graph-structures, we degenerate the Multi-Graph Multi-Label Learning into the Multi-Instance Multi-Label Learning in order to solve it by MIML-ELM (Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine). The performance study shows that our algorithm outperforms the competitors in terms of both effectiveness and efficiency. MDPI 2018-04-02 /pmc/articles/PMC7512760/ /pubmed/33265336 http://dx.doi.org/10.3390/e20040245 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhu, Zixuan
Zhao, Yuhai
Multi-Graph Multi-Label Learning Based on Entropy
title Multi-Graph Multi-Label Learning Based on Entropy
title_full Multi-Graph Multi-Label Learning Based on Entropy
title_fullStr Multi-Graph Multi-Label Learning Based on Entropy
title_full_unstemmed Multi-Graph Multi-Label Learning Based on Entropy
title_short Multi-Graph Multi-Label Learning Based on Entropy
title_sort multi-graph multi-label learning based on entropy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512760/
https://www.ncbi.nlm.nih.gov/pubmed/33265336
http://dx.doi.org/10.3390/e20040245
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