Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18577
Title: Semi-supervised ensemble classification in subspaces
Authors: Yu, G
Zhang, G
Yu, Z
Domeniconi, C
You, J 
Han, G
Keywords: Ensemble classification
Graph construction
High dimensional data
Semi-supervised classification
Subspaces
Issue Date: 2012
Publisher: Elsevier
Source: Applied soft computing, 2012, v. 12, no. 5, p. 1511-1522 How to cite?
Journal: Applied soft computing 
Abstract: Graph-based semi-supervised classification depends on a well-structured graph. However, it is difficult to construct a graph that faithfully reflects the underlying structure of data distribution, especially for data with a high dimensional representation. In this paper, we focus on graph construction and propose a novel method called semi-supervised ensemble classification in subspaces, SSEC in short. Unlike traditional methods that execute graph-based semi-supervised classification in the original space, SSEC performs semi-supervised linear classification in subspaces. More specifically, SSEC first divides the original feature space into several disjoint feature subspaces. Then, it constructs a neighborhood graph in each subspace, and trains a semi-supervised linear classifier on this graph, which will serve as the base classifier in an ensemble. Finally, SSEC combines the obtained base classifiers into an ensemble classifier using the majority-voting rule. Experimental results on facial images classification show that SSEC not only has higher classification accuracy than the competitive methods, but also can be effective in a wide range of values of input parameters.
URI: http://hdl.handle.net/10397/18577
ISSN: 1568-4946
EISSN: 1872-9681
DOI: 10.1016/j.asoc.2011.12.019
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