Stopping Criterion Design for Recursive Bayesian Classification: Analysis and Decision Geometry
Stopping Criterion Design for Recursive Bayesian Classification: Analysis and Decision Geometry
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DOI：
10.1109/tpami.2021.3075915
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发表时间：
2020-07
影响因子：
23.6
通讯作者：
Aziz Koçanaoğulları;M. Akçakaya;Deniz Erdoğmuş
中科院分区：
文献类型：
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作者：
Aziz Koçanaoğulları;M. Akçakaya;Deniz Erdoğmuş
基于递归贝叶斯分类更新的系统通过某些停止/终止标准限制证据收集的成本，并相应地执行决策。常规地，通常使用两个终止准则，其基于在（i）状态后验分布的最大值;和（ii）状态后验不确定性上的预定义阈值。在本文中，我们提出了一个几何解释的状态后验级数，因此，我们提供了一个逐点分析的缺点，使用这种传统的终止标准。例如，通过建议的几何解释，我们表明，定义在最大的状态后验的置信阈值遭受刚度，导致不必要的证据收集，而基于不确定性的阈值方法是脆弱的类别数和提前终止，如果一些国家的候选人已经发现是不利的。此外，这两种类型的终止方法忽略了后验更新的演变。然后，我们提出了一个新的停止/终止标准的几何洞察力，以克服这些传统方法的局限性，并提供了一个比较的决策精度和速度。我们验证我们的主张，使用模拟和使用真实的实验数据，通过大脑计算机接口打字系统。
Systems that are based on recursive Bayesian updates for classification limit the cost of evidence collection through certain stopping/termination criteria and accordingly enforce decision making. Conventionally, two termination criteria based on pre-defined thresholds over (i) the maximum of the state posterior distribution; and (ii) the state posterior uncertainty are commonly used. In this paper, we propose a geometric interpretation over the state posterior progression and accordingly we provide a point-by-point analysis over the disadvantages of using such conventional termination criteria. For example, through the proposed geometric interpretation we show that confidence thresholds defined over maximum of the state posteriors suffer from stiffness that results in unnecessary evidence collection whereas uncertainty based thresholding methods are fragile to number of categories and terminate prematurely if some state candidates are already discovered to be unfavorable. Moreover, both types of termination methods neglect the evolution of posterior updates. We then propose a new stopping/termination criterion with a geometrical insight to overcome the limitations of these conventional methods and provide a comparison in terms of decision accuracy and speed. We validate our claims using simulations and using real experimental data obtained through a brain computer interfaced typing system.