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Tree-Based Integration of One-versus-Some (OVS) Classifiers for Multiclass Classification

Ding, Yuejing; Zheng, Tian

Motivated by applications such as gene expression analysis, binary classification has achieved notable success. (e.g., cancer samples versus normal samples) When comes to multiclass classification, the extension is not straightforward. There has been two main directions on such extensions: 1) via a sequence of nested binary classifiers in a classification tree or 2) via classifier ensembles that integrate votes from all one-versus-all (OVA) classifiers or all all-pairs (AP) classifiers. In this article, we present a new way to combine both strategies in a multiclass classification.

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August 19, 2009

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This technical report was included in the Joint Statistical Meeting 2006 proceedings.

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