A feature selection method based on instance learning and cooperative subset search
Feature subset selection is a key problem in such data mining classification tasks.In practice,the filter methods ignore the correlations between genes which are prevalent in gene expression data,additionally,existing methods are not specially conceived to handle the small sample size of the data wh...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2017-06-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017122/ |
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| _version_ | 1841530138328563712 |
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| author | Xiaoyuan XU Li HUANG |
| author_facet | Xiaoyuan XU Li HUANG |
| author_sort | Xiaoyuan XU |
| collection | DOAJ |
| description | Feature subset selection is a key problem in such data mining classification tasks.In practice,the filter methods ignore the correlations between genes which are prevalent in gene expression data,additionally,existing methods are not specially conceived to handle the small sample size of the data which is one of the main causes of feature selection instability.In order to deal with these issues,a new hybrid,filter wrapper was proposed,and a cooperative subset search(CSS),was then researched with a classifier algorithm to represent an evaluation system of wrappers.The method was experimentally tested and compared with state-of-the-art algorithms based on several high-dimension allow sample size cancer data sets.Results show that the proposed approach outperforms other methods in terms of accuracy and stability of the selected subset. |
| format | Article |
| id | doaj-art-74c9718b177c4b4ea2c0acc4b481bc0d |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2017-06-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-74c9718b177c4b4ea2c0acc4b481bc0d2025-01-15T03:12:43ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012017-06-013310511359601861A feature selection method based on instance learning and cooperative subset searchXiaoyuan XULi HUANGFeature subset selection is a key problem in such data mining classification tasks.In practice,the filter methods ignore the correlations between genes which are prevalent in gene expression data,additionally,existing methods are not specially conceived to handle the small sample size of the data which is one of the main causes of feature selection instability.In order to deal with these issues,a new hybrid,filter wrapper was proposed,and a cooperative subset search(CSS),was then researched with a classifier algorithm to represent an evaluation system of wrappers.The method was experimentally tested and compared with state-of-the-art algorithms based on several high-dimension allow sample size cancer data sets.Results show that the proposed approach outperforms other methods in terms of accuracy and stability of the selected subset.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017122/feature selectionhybridsmall sampleclassificationstability |
| spellingShingle | Xiaoyuan XU Li HUANG A feature selection method based on instance learning and cooperative subset search Dianxin kexue feature selection hybrid small sample classification stability |
| title | A feature selection method based on instance learning and cooperative subset search |
| title_full | A feature selection method based on instance learning and cooperative subset search |
| title_fullStr | A feature selection method based on instance learning and cooperative subset search |
| title_full_unstemmed | A feature selection method based on instance learning and cooperative subset search |
| title_short | A feature selection method based on instance learning and cooperative subset search |
| title_sort | feature selection method based on instance learning and cooperative subset search |
| topic | feature selection hybrid small sample classification stability |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017122/ |
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