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Feature Genes Selection Using Fuzzy Rough Uncertainty Metric for Tumor Diagnosis
To select more effective feature genes, many existing algorithms focus on the selection and study of evaluation methods for feature genes, ignoring the accurate mapping of original information in data processing. Therefore, for solving this problem, a new model is proposed in this paper: rough uncer...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6369463/ https://www.ncbi.nlm.nih.gov/pubmed/30809269 http://dx.doi.org/10.1155/2019/6705648 |
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author | Xu, Jiucheng Wang, Yun Xu, Keqiang Zhang, Tianli |
author_facet | Xu, Jiucheng Wang, Yun Xu, Keqiang Zhang, Tianli |
author_sort | Xu, Jiucheng |
collection | PubMed |
description | To select more effective feature genes, many existing algorithms focus on the selection and study of evaluation methods for feature genes, ignoring the accurate mapping of original information in data processing. Therefore, for solving this problem, a new model is proposed in this paper: rough uncertainty metric model. First, the fuzzy neighborhood granule of the sample is constructed by combining the fuzzy similarity relation with the neighborhood radius in the rough set, and the rough decision is defined by using the fuzzy similarity relation and the decision equivalence class. Then, the fuzzy neighborhood granule and the rough decision are introduced into the conditional entropy, and the rough uncertainty metric model is proposed; in the meantime, the definition of measuring the significance of feature genes and the proof of some related theorems are given. To make this model tolerate noises in data, this paper introduces a variable precision model and discusses the selection of parameters. Finally, based on the rough uncertainty metric model, we design a feature genes selection algorithm and compare it with some existing similar algorithms. The experimental results show that the proposed algorithm can select the smaller feature genes subset with higher classification accuracy and verify that the model proposed in this paper is more effective. |
format | Online Article Text |
id | pubmed-6369463 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-63694632019-02-26 Feature Genes Selection Using Fuzzy Rough Uncertainty Metric for Tumor Diagnosis Xu, Jiucheng Wang, Yun Xu, Keqiang Zhang, Tianli Comput Math Methods Med Research Article To select more effective feature genes, many existing algorithms focus on the selection and study of evaluation methods for feature genes, ignoring the accurate mapping of original information in data processing. Therefore, for solving this problem, a new model is proposed in this paper: rough uncertainty metric model. First, the fuzzy neighborhood granule of the sample is constructed by combining the fuzzy similarity relation with the neighborhood radius in the rough set, and the rough decision is defined by using the fuzzy similarity relation and the decision equivalence class. Then, the fuzzy neighborhood granule and the rough decision are introduced into the conditional entropy, and the rough uncertainty metric model is proposed; in the meantime, the definition of measuring the significance of feature genes and the proof of some related theorems are given. To make this model tolerate noises in data, this paper introduces a variable precision model and discusses the selection of parameters. Finally, based on the rough uncertainty metric model, we design a feature genes selection algorithm and compare it with some existing similar algorithms. The experimental results show that the proposed algorithm can select the smaller feature genes subset with higher classification accuracy and verify that the model proposed in this paper is more effective. Hindawi 2019-01-27 /pmc/articles/PMC6369463/ /pubmed/30809269 http://dx.doi.org/10.1155/2019/6705648 Text en Copyright © 2019 Jiucheng Xu et al. http://creativecommons.org/licenses/by/4.0/ 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 Xu, Jiucheng Wang, Yun Xu, Keqiang Zhang, Tianli Feature Genes Selection Using Fuzzy Rough Uncertainty Metric for Tumor Diagnosis |
title | Feature Genes Selection Using Fuzzy Rough Uncertainty Metric for Tumor Diagnosis |
title_full | Feature Genes Selection Using Fuzzy Rough Uncertainty Metric for Tumor Diagnosis |
title_fullStr | Feature Genes Selection Using Fuzzy Rough Uncertainty Metric for Tumor Diagnosis |
title_full_unstemmed | Feature Genes Selection Using Fuzzy Rough Uncertainty Metric for Tumor Diagnosis |
title_short | Feature Genes Selection Using Fuzzy Rough Uncertainty Metric for Tumor Diagnosis |
title_sort | feature genes selection using fuzzy rough uncertainty metric for tumor diagnosis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6369463/ https://www.ncbi.nlm.nih.gov/pubmed/30809269 http://dx.doi.org/10.1155/2019/6705648 |
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