带噪声标签二分类的最优计算预算分配及其在仿真分析中的应用

Weizhi Liu, Haobin Li, L. Lee, E. P. Chew, Hui Xiao
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引用次数: 1

摘要

在本研究中,我们考虑了带有噪声标签的二分类的预算分配问题。通过观察标签的多个独立观测值,可以降低标签噪声,从而提高分类精度。因此,需要一种有效的预算分配策略来降低标签噪声,同时保证分类精度。在这项工作中研究了两个问题设置。假设我们不知道潜在的分类结构和标签,只能通过将其伯努利成功概率的样本平均值与给定阈值进行比较来确定。另一种情况假设具有不同标签的数据点可以用超平面分隔。针对这两种情况,提出了封闭形式的最优预算分配策略。仿真分析示例演示了如何将预算分配到不同的场景,以进一步改进最优决策函数的学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal Computing Budget Allocation for Binary Classification with Noisy Labels and its Applications on Simulation Analytics
In this study, we consider the budget allocation problem for binary classification with noisy labels. The classification accuracy can be improved by reducing the label noises which can be achieved by observing multiple independent observations of the labels. Hence, an efficient budget allocation strategy is needed to reduce the label noise and meanwhile guarantees a promising classification accuracy. Two problem settings are investigated in this work. One assumes that we do not know the underlying classification structures and labels can only be determined by comparing the sample average of its Bernoulli success probability with a given threshold. The other case assumes that data points with different labels can be separated by a hyperplane. For both cases, the closed-form optimal budget allocation strategies are developed. A simulation analytics example is used to demonstrate how the budget is allocated to different scenarios to further improve the learning of optimal decision functions.
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