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An Improved Algorithm for Feature Selection Based on Pairwise Constraint(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2011年01期
Page:
56-61
Research Field:
Publishing date:

Info

Title:
An Improved Algorithm for Feature Selection Based on Pairwise Constraint
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
Keywords:
machine learning; feature selection; pairwise constraint; classification
PACS:
TP181
DOI:
-
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．

References:

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Last Update: 2013-03-21