[1]杨杨,刘会东.一种基于成对约束的特征选择改进算法[J].南京师范大学学报(工程技术版),2011,11(01):056-61.
　Yang Yang,Liu Huidong.An Improved Algorithm for Feature Selection Based on Pairwise Constraint[J].Journal of Nanjing Normal University(Engineering and Technology),2011,11(01):056-61.
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一种基于成对约束的特征选择改进算法
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
11卷
期数:
2011年01期
页码:
056-61
栏目:
出版日期:
2011-03-09

文章信息/Info

Title:
An Improved Algorithm for Feature Selection Based on Pairwise Constraint
作者:
杨杨1; 刘会东2
1． 南京师范大学强化培养学院，江苏南京210046; 2． 南京师范大学计算机科学与技术学院，江苏南京210046
Author(s):
Yang Yang1; Liu Huidong2
1.Intensification Culture School,Nanjing Normal University,Nanjing 210046,China; 2.School of Computer Science and Technology,Nanjing Normal University,Nanjing 210046,China
关键词:
机器学习; 特征选择; 成对约束; 分类
Keywords:
machine learning; feature selection; pairwise constraint; classification
分类号:
TP181
摘要:
基于成对约束的特征选择算法通过度量单个特征的重要性得到一个特征序列,但由单个重要特征构成的特征子集未必是最有效的.为此,提出了一种基于成对约束的特征选择改进算法,该算法采用对特征子集进行度量的策略,逐步选择使新的特征子集最有效的特征,从而得到一个有效的特征序列.实验表明新提出的算法是有效可行的.
Abstract:
Feature selection is key issue in machine learning field． As compared with unsupervised feature selection methods，supervised feature selection approaches have more better performances． However，most of the existing supervised feature selection algorithms mainly aim at the cases using the labels as supervised information，here these methods are not applied to the cases with pairwise constraints． In the real application，it is more easier to get the pairwise constraints as comparing with getting labels． So the researchers proposed a feature selection based on pairwise constraint， the algorithm obtains a feature sequence by measuring the significance of each single feature，but in fact the feature subset combining by those more important features may be not an effective feature subset． Therefore，in this paper，we introduce an improved feature selection algorithm based on pairwise constraint，the newly developed algorithm focuses on evaluating the importance of a feature subset but not a single feature，that is，it uses the empty feature subset as starting point，and then gradually extends this feature subset by adding a most effective feature in every round，in this way an effective ranking feature list is obtained． Experimental results show that the newly proposed algorithm is flexible．

参考文献/References:

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备注/Memo

备注/Memo:
基金项目: 南京师范大学2010 年学生科学基金．通讯联系人: 刘会东，硕士，研究方向: 稀疏化和多任务学习． E-mail: huidong － liu@163． Com
更新日期/Last Update: 2013-03-21