A multi-class boosting method with direct optimization

Zhai, Shaodan; Xia, Tian; Wang, Shaojun

doi:10.1145/2623330.2623689

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…In the first one, named binary reduction, a multi-class problem is recast to a combination of a number of binary sub-problems, each to be solved with the original AdaBoost. Unfortunately, binary reduction is subject to a number of problems (Zhai et al, 2014). The second scheme plans to directly make use of native multi-class base classifiers, and use them in the context of a boosting procedure.…”

Section: Introductionmentioning

confidence: 99%

An analysis of boosted ensembles of binary fuzzy decision trees

Barsacchi¹,

Bechini²,

Marcelloni³

2020

Expert Systems with Applications

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Section: Introductionmentioning

confidence: 99%

An analysis of boosted ensembles of binary fuzzy decision trees

Barsacchi¹,

Bechini²,

Marcelloni³

2020

Expert Systems with Applications

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A Multi-label Ensemble Method Based on Minimum Ranking Margin Maximization

Zhai

Zhao

Xia

et al. 2015

2015 IEEE International Conference on Data Mining

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A Novel AdaBoost Framework With Robust Threshold and Structural Optimization

Zhang¹,

Yang²

2018

IEEE Trans. Cybern.

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The AdaBoost algorithm is a popular ensemble method that combines several weak learners to boost generalization performance. However, conventional AdaBoost.RT algorithms suffer from the limitation that the threshold value must be manually specified rather than chosen through a self-adaptive mechanism, which cannot guarantee a result in an optimal model for general cases. In this paper, we present a generic AdaBoost framework with robust threshold mechanism and structural optimization on regression problems. The error statistics of each weak learner on one given problem dataset is utilized to automate the choice of the optimal cut-off threshold value. In addition, a special single-layer neural network is employed to provide a second opportunity to further adjust the structure and strength the adaption capability of the AdaBoost regression model. Moreover, to consolidate the theoretical foundation of AdaBoost algorithms, we are the first to conduct a rigorous and comprehensive theoretical analysis on the proposed approach. We prove that the general bound on the empirical error with a fraction of training examples is always within a limited soft margin, which indicates that our novel algorithm can avoid over-fitting. We further analyze the bounds on the generalization error directly under probably approximately correct learning. The extensive experimental verifications on the UCI benchmarks have demonstrated that the performance of the proposed method is superior to other state-of-the-art ensemble and single learning algorithms. Furthermore, a real-world indoor positioning application has also revealed that the proposed method has higher positioning accuracy and faster speed.

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A multi-class boosting method with direct optimization

Cited by 4 publications

References 26 publications

An analysis of boosted ensembles of binary fuzzy decision trees

An analysis of boosted ensembles of binary fuzzy decision trees

A Multi-label Ensemble Method Based on Minimum Ranking Margin Maximization

A Novel AdaBoost Framework With Robust Threshold and Structural Optimization

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