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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >A Batch-Mode Active Learning Technique Based on Multiple Uncertainty for SVM Classifier
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A Batch-Mode Active Learning Technique Based on Multiple Uncertainty for SVM Classifier

机译：基于多不确定性的SVM分类器批量模式主动学习技术

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摘要

In this letter, we present a novel batch-mode active learning technique for solving multiclass classification problems by using the support vector machine classifier with the one-against-all architecture. The uncertainty of each unlabeled sample is measured by defining a criterion which not only considers the smallest distance to the decision hyperplanes but also takes into account the distances to other hyperplanes if the sample is within the margin of their decision boundaries. To select batch of most uncertain samples from all over the decision region, the uncertain regions of the classifiers are partitioned into multiple parts depending on the number of geometrical margins of binary classifiers passing on them. Then, a balanced number of most uncertain samples are selected from each part. To minimize the redundancy and keep the diversity among these samples, the kernel $k$-means clustering algorithm is applied to the set of uncertain samples, and the representative sample (medoid) from each cluster is selected for labeling. The effectiveness of the proposed method is evaluated by comparing it with other batch-mode active learning techniques existing in the literature. Experimental results on two different remote sensing data sets confirmed the effectiveness of the proposed technique.
机译：在这封信中，我们提出了一种新颖的批处理模式主动学习技术，该技术通过使用具有反对一切的结构的支持向量机分类器来解决多类分类问题。通过定义一个标准来测量每个未标记样本的不确定性，该准则不仅考虑到决策超平面的最小距离，而且还考虑如果样本处于其决策边界的范围内，则与其他超平面的距离。为了从整个决策区域中选择一批最不确定的样本，分类器的不确定区域根据通过它们的二元分类器的几何余量的数量划分为多个部分。然后，从每个部分中选择均衡数量的最不确定的样本。为了最小化冗余并保持这些样本之间的多样性，将内核$ k $ -means聚类算法应用于不确定样本集，并从每个聚类中选择代表性样本（类固醇）进行标记。通过将其与文献中存在的其他批处理模式主动学习技术进行比较，可以评估该方法的有效性。在两个不同的遥感数据集上的实验结果证实了该技术的有效性。

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