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成本曲线的点态精确自举分布

Pointwise Exact Bootstrap Distributions of Cost Curves
课程网址: http://videolectures.net/icml08_dugas_pebd/  
主讲教师: Charles Dugas
开课单位: 蒙特利尔大学
开课时间: 2008-08-29
课程语种: 英语
中文简介:
最近引入了成本曲线作为ROC曲线的替代或补充,以便可视化二元分类器的性能。对成本和ROC曲线都很重要的是置信区间的计算以及曲线本身,以便可以评估分类器性能的可靠性。计算两个分类器之间性能差异的置信区间允许确定一个分类器是否比另一个分类器表现得更好。在各种操作条件下获得成本的置信区间或两个成本之间的差异的简单过程是执行测试集的自举重新采样。在本文中,我们推导出这些值的精确bootstrap分布,并使用这些分布在各种操作条件下获得置信区间。这些置信区间的表现是根据覆盖精度来衡量的。模拟显示出优异的结果。
课程简介: Cost curves have recently been introduced as an alternative or complement to ROC curves in order to visualize binary classifiers performance. Of importance to both cost and ROC curves is the computation of confidence intervals along with the curves themselves so that the reliability of a classifier's performance can be assessed. Computing confidence intervals for the difference in performance between two classifiers allows to determine whether one classifier performs significantly better than another. A simple procedure to obtain confidence intervals for costs or the difference between two costs, under various operating conditions, is to perform bootstrap resampling of the testset. In this paper, we derive exact bootstrap distributions of these values and use these distributions to obtain confidence intervals, under various operating conditions. Performances of these confidence intervals are measured in terms of coverage accuracies. Simulations show excellent results.
关 键 词: 成本曲线; 置信区间; 覆盖精度
课程来源: 视频讲座网
最后编审: 2019-04-18:cwx
阅读次数: 77