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| Active Learning Algorithm Based on Neighborhood Entropy |
| WANG Zhen-Yu, WANG Xi-Zhao |
Key Laboratory of Machine Learning and Computational Intelligence,
College of Mathematics and Computer Science, Hebei University, Baoding 071002 |
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Abstract Neighborhood entropy is adopted as the sample selection criteria in active learning. The example with the highest entropy value is considered as the most uncertain one based on current nearest neighbor rule. And labeling the most uncertain example can achieve higher accuracy with fewer samples. An active learning algorithm based on neighborhood entropy is proposed. The scheme estimates entropy value of neighbor unlabeled sample and label the sample with the highest value. Experimental results show the example selection based on neighborhood entropy achieves higher accuracy compared with maximal distance sampling and random sampling.
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Received: 07 April 2010
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2011, Vol. 24 
