Robust boosting classification models with local sets of probability distributions

Robust classification models based on the ensemble methodology are proposed in the paper. The main feature of the models is that the precise vector of weights assigned for examples in the training set at each iteration of boosting is replaced by a local convex set of weight vectors. The minimax stra...

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Bibliographic Details
Published in:Knowledge-based systems 2014-05, Vol.61, p.59-75
Main Authors: Utkin, Lev V., Zhuk, Yulia A.
Format: Article
Language:English
Subjects:
Boosting
Classification
Imprecise model
Iterative methods
Knowledge base
Machine learning
Mathematical analysis
Mathematical models
Robust
Strategy
Vectors (mathematics)
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Summary:Robust classification models based on the ensemble methodology are proposed in the paper. The main feature of the models is that the precise vector of weights assigned for examples in the training set at each iteration of boosting is replaced by a local convex set of weight vectors. The minimax strategy is used for building weak classifiers at each iteration. The local sets of weights are constructed by means of imprecise statistical models. The proposed models are called RILBoost (Robust Imprecise Local Boost). Numerical experiments with real data show that the proposed models outperform the standard AdaBoost algorithm for several well-known data sets.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2014.02.007