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首页> 外文会议>Proceedings of the Third international conference on operations and supply chain management >A Promising Method of Knowledge Acquisition Using a Combination of Bayesian Network and Rough Set Theory
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A Promising Method of Knowledge Acquisition Using a Combination of Bayesian Network and Rough Set Theory

机译：贝叶斯网络与粗糙集理论相结合的有前途的知识获取方法

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摘要

The determinant of survival in the knowledge-based economy is knowledge development and management, which usually starts with knowledge acquisition followed by knowledge organization and utilization. Although several studies demonstrate that data mining techniques and the rough sets theory (RST) are useful to knowledge acquisition, few people really enjoy or benefit from them in daily work and life. This is primarily because we lack a practical way of implementing them, a method which can reliably provide us with certain results in knowledge acquisition. This paper proposes a knowledge acquisition process that enables us to gain knowledge useful for decision support through a combination of Bayesian networks and the RST. An empirical study is presented to illustrate the application of the proposed method. According to the findings of this study, management implications and conclusions are discussed.
机译：知识型经济中生存的决定因素是知识的开发和管理，通常从知识获取开始，然后是知识的组织和利用。尽管多项研究表明，数据挖掘技术和粗糙集理论（RST）对于知识获取很有用，但很少有人真正在日常工作和生活中享受或从中受益。这主要是因为我们缺乏实现它们的实用方法，该方法可以可靠地为我们提供知识获取方面的某些结果。本文提出了一种知识获取过程，该过程使我们能够通过贝叶斯网络和RST的结合获得对决策支持有用的知识。进行了一项实证研究，以说明该方法的应用。根据这项研究的结果，讨论了管理的意义和结论。

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