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arXiv 1304.1127cs.AI
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信度函数在不确定性管理和知识获取中的应用

Using Belief Functions for Uncertainty Management and Knowledge Acquisition: An Expert Application

University of Guelph(圭尔夫大学)

Mary McLeish, P. Yao, T. Stirtzinger

摘要:

本文介绍了最近的工作正在进行的项目在医学诊断圭尔夫大学。一个领域的专家是不是很好地查明一个单一的疾病的结果进行了探讨。在线医疗数据可在相对较短的时间内获得。信度函数(Dempster-Shafer理论)首先从数据中提取,然后根据专家意见进行修改。这样做的几种方法进行了比较,结果表明,一个配方在统计上优于其他,包括谢弗建议的方法。专家的意见和统计得出的信息之间的依赖性症状进行了比较。讨论了使用不确定性管理技术作为从数据中获取知识获取的方法的好处。

英文摘要:

This paper describes recent work on an ongoing project in medical diagnosis at the University of Guelph. A domain on which experts are not very good at pinpointing a single disease outcome is explored. On-line medical data is available over a relatively short period of time. Belief Functions (Dempster-Shafer theory) are first extracted from data and then modified with expert opinions. Several methods for doing this are compared and results show that one formulation statistically outperforms the others, including a method suggested by Shafer. Expert opinions and statistically derived information about dependencies among symptoms are also compared. The benefits of using uncertainty management techniques as methods for knowledge acquisition from data are discussed.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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