A Direct Boosting Approach for Semi-supervised Classification
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Machine Learning. Pages 4033-4039.
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.
@inproceedings{ijcai2015p565,
title = {A Direct Boosting Approach for Semi-supervised Classification},
author = {Zhai, Shaodan and Xia, Tian and Li, Zhongliang and Wang, Shaojun},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {4033--4039},
year = {2015},
month = {7},
note = {Special Track on Machine Learning},
url = {https://ijcai.org/proceedings/2015/565},
}
TY - CPAPER TI - A Direct Boosting Approach for Semi-supervised Classification AU - Zhai, Shaodan AU - Xia, Tian AU - Li, Zhongliang AU - Wang, Shaojun T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 4033 EP - 4039 UR - https://ijcai.org/proceedings/2015/565 ER -Copied
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