Nikunj C. Oza's Publications

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Key Real-World Applications of Classifier Ensembles

Key Real-World Applications of Classifier Ensembles. Nikunj C. Oza and Kagan Tumer. Information Fusion, Special Issue on Applications of Ensemble Methods, 9(1):4–20, 2008.

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Abstract

Broad classes of statistical classification algorithms have beendeveloped and applied successfully to a wide range of real worlddomains. In general, ensuring that the particular classificationalgorithm matches the properties of the data is crucial inproviding results that meet the needs of the particular applicationdomain. One way in which the impact of this algorithm/applicationmatch can be alleviated is by using ensembles of classifiers, wherea variety of classifiers (either different types of classifiers ordifferent instantiations of the same classifier) are pooled before afinal classification decision is made. Intuitively, classifierensembles allow the different needs of a difficult problem to behandled by classifiers suited to those particular needs.Mathematically, classifier ensembles provide an extra degree offreedom in the classical bias/variance tradeoff, allowing solutionsthat would be difficult (if not impossible) to reach with only asingle classifier. Because of these advantages, classifier ensembles have been applied to many difficult real world problems. In this paper, we surveyselect applications of ensemble methods to problems that havehistorically been most representative of the difficulties inclassification. In particular, we survey applications of ensemblemethods to remote sensing, person recognition, one vs. allrecognition, and medicine.

BibTeX Entry

@article{oztu08,
	author = {Nikunj C. Oza and Kagan Tumer},
	title = {Key Real-World Applications of Classifier Ensembles},
	journal = {Information Fusion, Special Issue on Applications of Ensemble Methods},
	volume = {9},
	number = {1},
	pages = {4-20},
	abstract = {Broad classes of statistical classification algorithms have been
developed and applied successfully to a wide range of real world
domains. In general, ensuring that the particular classification
algorithm matches the properties of the data is crucial in
providing results that meet the needs of the particular application
domain. One way in which the impact of this algorithm/application
match can be alleviated is by using ensembles of classifiers, where
a variety of classifiers (either different types of classifiers or
different instantiations of the same classifier) are pooled before a
final classification decision is made. Intuitively, classifier
ensembles allow the different needs of a difficult problem to be
handled by classifiers suited to those particular needs.
Mathematically, classifier ensembles provide an extra degree of
freedom in the classical  bias/variance tradeoff, allowing solutions
that would be difficult (if not impossible) to reach with only a
single classifier. Because of these advantages, classifier ensembles have been applied to many difficult real world problems. In this paper, we survey
select applications of ensemble methods to problems that have
historically been most representative of the difficulties in
classification. In particular, we survey applications of ensemble
methods to remote sensing, person recognition, one vs. all
recognition, and medicine.},
	bib2html_pubtype = {Journal Article},
	bib2html_rescat = {Ensemble Learning},
	year = {2008}
}

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