ML Anthology

A Direct Boosting Approach for Semi-Supervised Classification

Shaodan Zhai, Tian Xia, Zhongliang Li, Shaojun Wang
IJCAI 2015 pp. 4025-4032
/ijcai/2015/zhai2015ijcai-direct/

Abstract

We introduce a semi-supervised boosting approach (SSDBoost), which directly minimizes the classification errors and maximizes the margins on both labeled and unlabeled samples, without resorting to any upper bounds or approximations. A two-step algorithm based on coordinate descent/ascent is proposed to implement SSDBoost. Experiments on a number of UCI datasets and synthetic data show that SSDBoost gives competitive or superior results over the state-of-the-art supervised and semi-supervised boosting algorithms in the cases that the labeled data is limited, and it is very robust in noisy cases.

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Cite

Text

Zhai et al. "A Direct Boosting Approach for Semi-Supervised Classification." International Joint Conference on Artificial Intelligence, 2015.

Markdown

[Zhai et al. "A Direct Boosting Approach for Semi-Supervised Classification." International Joint Conference on Artificial Intelligence, 2015.](https://mlanthology.org/ijcai/2015/zhai2015ijcai-direct/)

BibTeX

@inproceedings{zhai2015ijcai-direct,
  title     = {{A Direct Boosting Approach for Semi-Supervised Classification}},
  author    = {Zhai, Shaodan and Xia, Tian and Li, Zhongliang and Wang, Shaojun},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2015},
  pages     = {4025-4032},
  url       = {https://mlanthology.org/ijcai/2015/zhai2015ijcai-direct/}
}