| 双向自动分支界限特征选择算法 |
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| 引用本文： | 杨胜,施鹏飞.双向自动分支界限特征选择算法[J].上海大学学报(英文版),2005,9(3):244-248. |
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| 作者姓名： | 杨胜 施鹏飞 |
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| 作者单位： | InstituteofImageProcessingandPatternRecognition,ShanghaiJiaotongUniversity,Shanghai200030,P.R.China |
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| 基金项目： | theNationalNatureScienceFoundationofChina(GrantNo.60075007) |
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| 摘 要： | Feature selection ks a process where a miniraal feature subset ks selected from an original feature set according to a certain measure. In this paper, feature relevancy ks defined by an inconsistency rate. A bidirectional automated branch and bound algorithm is presented. It is a new complete search algorithm for feature selection, which performs feature deletion and feature addition in parallel.Its bound ks determined by inconsistency rate of the original feature set, hence termed as ‘automated‘. Experimental study shows that it ks fit for feature selection.
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| 关 键 词： | 特征选择 模式分类 数据采集 自动控制 |
| 收稿时间： | 4 November 2003 |
Bidirectional automated branch and bound algorithm for feature selection |
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| Yang?Sheng,Shi?Peng-fei.Bidirectional automated branch and bound algorithm for feature selection[J].Journal of Shanghai University(English Edition),2005,9(3):244-248. |
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| Authors: | Yang Sheng Shi Peng-fei |
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| Institution: | Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, P. R. China |
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| Abstract: | Feature selection is a process where a minimal feature subset is selected from an original feature set according to a certain
measure. In this paper, feature relevancy is defined by an inconsistency rate. A bidirectional automated branch and bound
algorithm is presented. It is a new complete search algorithm for feature selection, which performs feature deletion and feature
addition in parallel. Its bound is determined by inconsistency rate of the original feature set, hence termed as ‘automated’.
Experimental study shows that it is fit for feature selection.
Project supported by the National Nature Science Foundation of China(Grant No. 60075007) |
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| Keywords: | feature selection pattern classification data mining machine learning |
| 本文献已被 CNKI 维普 万方数据 SpringerLink 等数据库收录！ |
