NDLI: Classification in High-Dimensional Feature Spaces: Random Subsample Ensemble

Content Provider	IEEE Xplore Digital Library
Author	Serpen, G. Pathical, S.
Copyright Year	2009
Abstract	This paper presents application of machine learning ensembles, which randomly project the original high dimensional feature space onto multiple lower dimensional feature subspaces, to classification problems with high-dimensional feature spaces. The motivation is to address challenges associated with algorithm scalability, data sparsity and information loss due to the so-called curse of dimensionality. The original high dimensional feature space is randomly projected onto a number of lower-dimensional feature subspaces. Each of these subspaces constitutes the domain of a classification subtask, and is associated with a base learner within an ensemble machine-learner context. Such an ensemble conceptualization is called as random subsample ensemble. Simulation results performed on data sets with up to 20,000 features indicate that the random subsample ensemble classifier performs comparably to other benchmark machine learners based on performance measures of prediction accuracy and cpu time. This finding establishes the feasibility of the ensemble and positions it to tackle classification problems with even much higher dimensional feature spaces.
Starting Page	740
Ending Page	745
File Size	292681
Page Count	6
File Format	PDF
ISBN	9780769539263
DOI	10.1109/ICMLA.2009.26
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Performance evaluation Machine learning algorithms Scalability curse of dimensionality Application software Computational complexity Computer science random subspace high dimensional feature space Space technology ensemble classification Machine learning Computational efficiency Principal component analysis
Content Type	Text
Resource Type	Article

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