Paper Info
Title
Enriching the random subspace method with margin theory - a solution for the high-dimensional classification task.
Abstract
The random subspace method (RSM) has proved its excellence in numbers of pattern recognition tasks. However, the standard RSM is limited owing to the randomness in its feature selection procedure that is likely to lead to feature subset having poor class separability. In this paper, a proposal for a margin-based criterion has been presented for the evaluation of the true significance of the features, together with the true classification ability of base classifiers, so that both the training phase and integration phase of standard RSM could be enhanced. In the training phase, the random feature selection procedure is enhanced using a weighted random feature selection procedure, in order to improve the classification ability of the base classifier. In the integration phase, the simple majority voting strategy is enhanced using a weighted majority voting strategy for the purpose of assigning the base classifiers with poor classification ability to the lower voting weights. Experimental results on 30 benchmark datasets, together with 6 high-dimensional datasets prove that the recommended approach is capable of better providing classification ability to the usual classification task, in addition to the high-dimensional classification task.
Year
DOI
Venue
2018
10.1080/09540091.2018.1512556
CONNECTION SCIENCE
Keywords
Field
DocType
Random subspace method,wrRSM,margin theory,weighted majority voting,high-dimensional problem
High dimensional problem,Feature selection,Voting,Random subspace method,Computer science,Artificial intelligence,Majority rule,Classifier (linguistics),Class separability,Machine learning,Randomness
Journal
Volume
Issue
ISSN
30.0
4
0954-0091
Citations 
PageRank 
References 
0
0.34
24
Authors
4
Name
Order
Citations
PageRank
Hongyan Xu111.35
Tao Lin2337.87
Yingtao Xie331.38
Zhi Chen413732.27