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A Novel Algorithm for Imbalance Data Classification Based on Neighborhood Hypergraph
The classification problem for imbalance data is paid more attention to. So far, many significant methods are proposed and applied to many fields. But more efficient methods are needed still. Hypergraph may not be powerful enough to deal with the data in boundary region, although it is an efficient...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4144305/ https://www.ncbi.nlm.nih.gov/pubmed/25180211 http://dx.doi.org/10.1155/2014/876875 |
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author | Hu, Feng Liu, Xiao Dai, Jin Yu, Hong |
author_facet | Hu, Feng Liu, Xiao Dai, Jin Yu, Hong |
author_sort | Hu, Feng |
collection | PubMed |
description | The classification problem for imbalance data is paid more attention to. So far, many significant methods are proposed and applied to many fields. But more efficient methods are needed still. Hypergraph may not be powerful enough to deal with the data in boundary region, although it is an efficient tool to knowledge discovery. In this paper, the neighborhood hypergraph is presented, combining rough set theory and hypergraph. After that, a novel classification algorithm for imbalance data based on neighborhood hypergraph is developed, which is composed of three steps: initialization of hyperedge, classification of training data set, and substitution of hyperedge. After conducting an experiment of 10-fold cross validation on 18 data sets, the proposed algorithm has higher average accuracy than others. |
format | Online Article Text |
id | pubmed-4144305 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-41443052014-09-01 A Novel Algorithm for Imbalance Data Classification Based on Neighborhood Hypergraph Hu, Feng Liu, Xiao Dai, Jin Yu, Hong ScientificWorldJournal Research Article The classification problem for imbalance data is paid more attention to. So far, many significant methods are proposed and applied to many fields. But more efficient methods are needed still. Hypergraph may not be powerful enough to deal with the data in boundary region, although it is an efficient tool to knowledge discovery. In this paper, the neighborhood hypergraph is presented, combining rough set theory and hypergraph. After that, a novel classification algorithm for imbalance data based on neighborhood hypergraph is developed, which is composed of three steps: initialization of hyperedge, classification of training data set, and substitution of hyperedge. After conducting an experiment of 10-fold cross validation on 18 data sets, the proposed algorithm has higher average accuracy than others. Hindawi Publishing Corporation 2014 2014-08-11 /pmc/articles/PMC4144305/ /pubmed/25180211 http://dx.doi.org/10.1155/2014/876875 Text en Copyright © 2014 Feng Hu et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Hu, Feng Liu, Xiao Dai, Jin Yu, Hong A Novel Algorithm for Imbalance Data Classification Based on Neighborhood Hypergraph |
title | A Novel Algorithm for Imbalance Data Classification Based on Neighborhood Hypergraph |
title_full | A Novel Algorithm for Imbalance Data Classification Based on Neighborhood Hypergraph |
title_fullStr | A Novel Algorithm for Imbalance Data Classification Based on Neighborhood Hypergraph |
title_full_unstemmed | A Novel Algorithm for Imbalance Data Classification Based on Neighborhood Hypergraph |
title_short | A Novel Algorithm for Imbalance Data Classification Based on Neighborhood Hypergraph |
title_sort | novel algorithm for imbalance data classification based on neighborhood hypergraph |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4144305/ https://www.ncbi.nlm.nih.gov/pubmed/25180211 http://dx.doi.org/10.1155/2014/876875 |
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