Journal
PATTERN RECOGNITION
Volume 47, Issue 12, Pages 3931-3940Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2014.06.008
Keywords
AdaBoost; Bayes; Probability estimation; Prediction
Funding
- Aeronautical Science Foundation of China [20115169016]
- General Armament Department Pre-research Foundation of China [9140C460302130C46173]
- Natural Science Foundation of Jiangsu Province of China [BK20131296]
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It is a general viewpoint that AdaBoost classifier has excellent performance on classification problems but could not produce good probability estimations. In this paper we put forward a theoretical analysis of probability estimation model and present some verification experiments, which indicate that AdaBoost can be used for probability estimation. With the theory, we suggest some useful measures for using AdaBoost algorithms properly. And then we deduce a probability estimation model for multiclass classification by pairwise coupling. Unlike previous approximate methods, we provide an analytical solution instead of a special iterative procedure. Moreover, a new problem that how to get a robust prediction with classifier scores is proposed. Experiments show that the traditional predict framework, which chooses one with the highest score from all classes as the prediction, is not always good while our model performs well. (C) 2014 Elsevier Ltd. All rights reserved.
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