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Consensus and Reliability: The Case of Two Binary Classifiers ?

机译：共识和可靠性：两个二进制分类器的情况 ？

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摘要

In this paper we consider the problem of estimating the probability of misclas-sification when consensus is achieved between two binary classifiers that are trained on the same training set. Firstly, it is shown that, under consensus, the probability of misclassification compares favourably with that of the best of the two classifiers. Secondly, we provide accurate, and yet simple to compute, estimates of the probability of consensus and the probability of misclassification under consensus. This paper provides a theoretical basis for these estimates and demonstrates their accuracy by simulation results on a synthetic data set and on a medical data set for breast cancer cell classification.
机译：在本文中，我们考虑估计在同一训练集培训的两个二元分类器之间的共识时估算Misclas的概率。首先，表明，在共识中，错误分类的概率与两个分类器中最好的概率有利地比较。其次，我们提供准确，但简单的计算，估计共识的概率以及在共识中错误分类的可能性。本文为这些估计提供了理论依据，并通过仿真结果和乳腺癌细胞分类的医学数据集进行了仿真结果来证明其准确性。

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