SPLBoost: An Improved Robust Boosting Algorithm Based on Self-Paced Learning
| Autor: | Qian Zhao, Zongben Xu, Kaidong Wang, Yao Wang, Deyu Meng, Xiuwu Liao |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Boosting (machine learning)
Computer science 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Robustness (computer science) 0202 electrical engineering electronic engineering information engineering AdaBoost Electrical and Electronic Engineering Self paced 0105 earth and related environmental sciences business.industry Computer Science Applications Exponential function Human-Computer Interaction ComputingMethodologies_PATTERNRECOGNITION Control and Systems Engineering Outlier 020201 artificial intelligence & image processing Artificial intelligence business LogitBoost computer Software Information Systems |
| Zdroj: | IEEE Transactions on Cybernetics. 51:1556-1570 |
| ISSN: | 2168-2275 2168-2267 |
| DOI: | 10.1109/tcyb.2019.2957101 |
| Popis: | It is known that boosting can be interpreted as an optimization technique to minimize an underlying loss function. Specifically, the underlying loss being minimized by the traditional AdaBoost is the exponential loss, which proves to be very sensitive to random noise/outliers. Therefore, several boosting algorithms, e.g., LogitBoost and SavageBoost, have been proposed to improve the robustness of AdaBoost by replacing the exponential loss with some designed robust loss functions. In this article, we present a new way to robustify AdaBoost, that is, incorporating the robust learning idea of self-paced learning (SPL) into the boosting framework. Specifically, we design a new robust boosting algorithm based on the SPL regime, that is, SPLBoost, which can be easily implemented by slightly modifying off-the-shelf boosting packages. Extensive experiments and a theoretical characterization are also carried out to illustrate the merits of the proposed SPLBoost. |
| Databáze: | OpenAIRE |
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