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On the Consistency of Multiclass Classification Methods

Journal of Machine Learning Research, Vol. 8 (May 2007), pp. 1007-1025.

X Abstract

Binary classification is a well studied special case of the classification problem. Statistical properties of binary classifiers, such as consistency, have been investigated in a variety of settings. Binary classification methods can be generalized in many ways to handle multiple classes. It turns out that one can lose consistency in generalizing a binary classification method to deal with multiple classes. We study a rich family of multiclass methods and provide a necessary and sufficient condition for their consistency. We illustrate our approach by applying it to some multiclass methods proposed in the literature.

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This article has been bookmarked 4 times, initially on 2009-05-21.

2009-07-02 User shivak , 1 note

A very useful extension of classification calibration to the multiclass setting, along with some equivalent, practically verifiable conditions and an example of a consistent loss function.

2009-07-02 02:16:07
2009-06-24 Group Statistical Machine Learning
User mdreid
2009-05-21 User sdvillal
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