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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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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. |
format | Online Article Text |
id | pubmed-7512760 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT zhuzixuan multigraphmultilabellearningbasedonentropy AT zhaoyuhai multigraphmultilabellearningbasedonentropy |