Subspace ensembles for classification

Shiliang Sun and Changshui Zhang

Physica A: Statistical Mechanics and its Applications, 2007, vol. 385, issue 1, 199-207

Abstract: Ensemble learning constitutes one of the principal current directions in machine learning and data mining. In this paper, we explore subspace ensembles for classification by manipulating different feature subspaces. Commencing with the nature of ensemble efficacy, we probe into the microcosmic meaning of ensemble diversity, and propose to use region partitioning and region weighting to implement effective subspace ensembles. Individual classifiers possessing eminent performance on a partitioned region reflected by high neighborhood accuracies are deemed to contribute largely to this region, and are assigned large weights in determining the labels of instances in this area. A robust algorithm “Sena” that incarnates the mechanism is presented, which is insensitive to the number of nearest neighbors chosen to calculate neighborhood accuracies. The algorithm exhibits improved performance over the well-known ensembles of bagging, AdaBoost and random subspace. The difference of its effectivity with varying base classifiers is also investigated.

Keywords: Multiple-classifier system; Feature selection; Ensemble learning; Ensemble diversity (search for similar items in EconPapers)
Date: 2007
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437107005304
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:385:y:2007:i:1:p:199-207

DOI: 10.1016/j.physa.2007.05.010

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().