Optimal Analysis of Boundary-Uncertainty-Based Classifier Selection Method
Optimal Analysis of Boundary-Uncertainty-Based Classifier Selection Method
复制标题
基于边界不确定性的分类器选择方法的优化分析
DOI：
10.1145/3297067.3297076
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Shigeru Katagiri
中科院分区：
文献类型：
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作者：
David Ha;Hideyuki Watanabe;Yuya Tomotoshi;Emilie Delattre;Shigeru Katagiri
我们提出了一种新的方法，选择一个最佳的分类器模型的参数状态，通过估计类边界的不确定性度量评估，而不是估计的分类错误概率。我们的方法的一个关键特征是它有可能在没有单独的验证样本集的情况下执行分类器参数状态选择，该验证样本集可以很容易地应用于任何合理类型的分类器模型，而不像传统方法那样经常需要验证样本集或有时不太实用。本文首先总结了我们的方法及其实验结果，介绍了后验概率估计过程的数学形式化，然后给出了估计过程的收敛性，最后证明了我们的方法在训练样本有限的情况下的最优性。
We proposed a novel method that selects an optimal classifier model's parameter status through the uncertainty measure evaluation of the estimated class boundaries instead of an estimation of the classification error probability. A key feature of our method is its potential to perform a classifier parameter status selection without a separate validation sample set that can be easily applied to any reasonable type of classifier model, unlike traditional approaches that often need a validation sample set or are sometimes less practical. In this paper, we first summarize our method and its experimental evaluation results and introduce the mathematical formalization for the posterior probability estimation procedure adopted in it. Then we show the convergence property of the estimation procedure and finally demonstrate our method's optimality in a practical situation where only a finite number of training samples are available.