Active Learning with Misclassification Sampling Based on Committee
Active Learning with Misclassification Sampling Based on Committee
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DOI：
10.1142/s0218488508005248
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发表时间：
2008-04
期刊：
影响因子：
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通讯作者：
Jun Long;Jianping Yin;En Zhu;Wentao Zhao
中科院分区：
文献类型：
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作者：
Jun Long;Jianping Yin;En Zhu;Wentao Zhao
主动学习是减少归纳学习问题数据收集成本的一种重要方法，它只对最有信息的实例进行采样进行标记。我们在这里重点讨论如何选择这些信息量最大的实例的抽样标准。本文主要做了三方面的贡献。首先，与现有的将版本空间体积减半的采样策略不同，我们提出了将版本空间体积减半以上的采样策略，假设目标函数选择在版本空间上的非均匀分布中。其次，我们提出了对最有可能被错误分类的实例进行抽样的想法。第三，我们开发了一种名为CBMPMS（基于委员会的最可能错误分类抽样）的抽样方法，该方法对当前分类器错误分类概率最大的实例进行抽样。将所提出的CBMPMS方法与现有的主动学习方法进行比较，当分类器达到相同的准确率时，前者的采样次数比后者少。实验表明，在大多数选定的数据集上，该方法优于传统的采样方法。
Active learning is an important approach to reduce data-collection costs for inductive learning problems by sampling only the most informative instances for labeling. We focus here on the sampling criterion for how to select these most informative instances. Three contributions are made in this paper. First, in contrast to the leading sampling strategy of halving the volume of version space, we present the sampling strategy of reducing the volume of version space by more than half with the assumption of target function being chosen from nonuniform distribution over version space. Second, we propose the idea of sampling the instances that would be most possibly misclassified. Third, we develop a sampling method named CBMPMS (Committee Based Most Possible Misclassification Sampling) which samples the instances that have the largest probability to be misclassified by the current classifier. Comparing the proposed CBMPMS method with the existing active learning methods, when the classifiers achieve the same accuracy, the former method will sample fewer times than the latter ones. The experiments show that the proposed method outperforms the traditional sampling methods on most selected datasets.