分类准确度置信区间的校准

S. Magnussen
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引用次数: 1

摘要

从复杂调查设计下获得的样本估计的比例的名义95%置信区间的覆盖率,或从两个随机变量的比率估计的比例,可能显著偏离其目标。对于由单个二元研究变量衍生的比例的间隔,存在有效的校准方法,但对于主题分类精度的估计却没有。为了在土地覆盖制图的背景下促进置信区间的校准,本研究首先说明了标准置信区间的覆盖不足和覆盖过高的常见问题,然后提出了一种简单而快速的校准方法,这种方法往往会提高覆盖范围。该演示是通过从包含四个类的分类地图中模拟采样进行的,其中一个参考类为排列在正方形阵列中的160,000个单元中的每个单元所知。模拟包括四种用于精度评估的常见概率抽样设计和三种样本量。在估计用户(UA)和生产者的准确性(PA)以及估计班级面积比例方面,都存在统计上显著的覆盖过高和覆盖不足。在较小的样本量和两个聚类抽样设计下,UA和PA的贝叶斯区间校准是最有效的。
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Calibration of a Confidence Interval for a Classification Accuracy
Coverage of nominal 95% confidence intervals of a proportion estimated from a sample obtained under a complex survey design, or a proportion estimated from a ratio of two random variables, can depart significantly from its target. Effective calibration methods exist for intervals for a proportion derived from a single binary study variable, but not for estimates of thematic classification accuracy. To promote a calibration of confidence intervals within the context of land-cover mapping, this study first illustrates a common problem of under and over-coverage with standard confidence intervals, and then proposes a simple and fast calibration that more often than not will improve coverage. The demonstration is with simulated sampling from a classified map with four classes, and a reference class known for every unit in a population of 160,000 units arranged in a square array. The simulations include four common probability sampling designs for accuracy assessment, and three sample sizes. Statistically significant over- and under-coverage was present in estimates of user’s (UA) and producer’s accuracy (PA) as well as in estimates of class area proportion. A calibration with Bayes intervals for UA and PA was most efficient with smaller sample sizes and two cluster sampling designs.
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