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Batch Mode Active Learning Algorithm Combining with Self-training for Multiclass Brain-computer Interfaces |
Minyou Chen, Xuemin Tan |
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Abstract In this paper, an batch mode active learning algorithm combining
with the benefits of self-training for solving the multiclass
Brain-computer Interface (BCI) problem, which initially only needs a
small set of labeled samples to train classifiers. The algorithm
applied active learning to select the most informative samples and
self-training to select the most high confidence samples,
respectively, according to the proposed novel uncertainty criterion
and confidence criterion for boosting the performance of the
classifier. Experiments on the Dataset 2a of the BCI Competition IV,
which demonstrate our method achieves more improvement than Active
Learning (AL) and Random Sampling (RS) when the same amount of human
effort is sacrificed.
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DOI:10.12733/jics20105675 |
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