摘要翻译：
对对象进行排序是组织数据的一个简单而自然的过程。它通常是通过根据每个对象与手头问题的相关性为每个对象分配一个质量分数来执行的。当资源有限，需要选择最相关对象的子集进行进一步处理时，排序被广泛用于对象选择。在现实世界的情况下，对象的分数往往是从噪声测量中计算出来的，这给排序的可靠性带来了怀疑。我们介绍了一种分析方法来评估噪声水平对排序可靠性的影响。我们用两个相似度度量Top-K-List重叠和Kendall的tau度量进行可靠性评估，结果表明前者比后者对噪声更敏感。我们将我们的方法应用于几种癌症类型的一系列微阵列实验中的基因选择。结果表明，从这些实验中得到的列表的可靠性很差，而获得合理稳定的top-k列表所需的实验规模比现有的大得多。模拟结果支持我们的分析结果。
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英文标题：
《Ranking Under Uncertainty》
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作者：
Or Zuk, Liat Ein-Dor, Eytan Domany
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  Ranking objects is a simple and natural procedure for organizing data. It is often performed by assigning a quality score to each object according to its relevance to the problem at hand. Ranking is widely used for object selection, when resources are limited and it is necessary to select a subset of most relevant objects for further processing. In real world situations, the object's scores are often calculated from noisy measurements, casting doubt on the ranking reliability. We introduce an analytical method for assessing the influence of noise levels on the ranking reliability. We use two similarity measures for reliability evaluation, Top-K-List overlap and Kendall's tau measure, and show that the former is much more sensitive to noise than the latter. We apply our method to gene selection in a series of microarray experiments of several cancer types. The results indicate that the reliability of the lists obtained from these experiments is very poor, and that experiment sizes which are necessary for attaining reasonably stable Top-K-Lists are much larger than those currently available. Simulations support our analytical results.
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PDF链接：
https://arxiv.org/pdf/1206.5280