基于近邻熵的主动学习算法

在主动学习中，采用近邻熵 (Neighborhood Entropy) 作为样例的挑选标准，熵值最大的样例体现基于近邻分类规则，最无法确定该样例的类标。而标注不确定性高的样例可用尽量少的样例获得较高的分类性能。文中提出一种基于近邻熵的主动学习算法。该算法首先计算未标注样例的近邻样例类别熵，然后挑选熵值最大样例的进行标注。实验表明，基于近邻熵挑选样例进行标注，较基于最大距离 (Maximal Distance) 挑选和随机样例挑选可获得更高的分类性能。

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.