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Robust Model-Free Multiclass Probability Estimation
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DOI：10.1198/jasa.2010.tm09107.png)
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Abstract
En 中文
Classical statistical approaches for multiclass probability estimation are typically based on regression technique, such as multiple logistic regression, or density estimation approaches such as linear discriminant analysis (LDA) and quadratic discriminant analysis (ODA) These methods often make certain assumptions on the form of probability functions or on the underlying distributions of subclasses In this article. we develop a model-free procedure to estimate multiclass probabilities based on large-margin classifiers In particular, the new estimation scheme is employed by solving a series of weighted large-mail:in classifiers and then systematically extracting the probability information from these multiple classification rules A main advantage of the proposed probability estimation technique is that it does not impose any strong parametric assumption on the underlying distribution and can be applied for a wide range of large-margin classification methods A general computational algorithm is developed for class probability estimation Furthermore, we establish asymptotic consistency of the probability estimates Both simulated and real data examples are presented to illustrate competitive performance of the new approach and compare it with several other existing methods
Keywords:
Fisher consistency
Hard classification
Multicategory classification
Probability estimation
Soft classification
SVM
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