Knowledge acquisition in vague objective information systems based on rough sets
Knowledge acquisition in vague objective information systems based on rough sets
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DOI：
10.1111/j.1468-0394.2010.00512.x
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发表时间：
2010-05
期刊：
影响因子：
3.3
通讯作者：
Lin Feng;Guoyin Wang;Xinxin Li
中科院分区：
文献类型：
--
作者：
Lin Feng;Guoyin Wang;Xinxin Li
摘要：日益增长的模糊信息对模糊数据集的分析提出了新的理论、技术和工具的需求，提出了新的挑战。本文研究了如何基于粗糙集理论从模糊客观信息系统中提取知识。首先将粗糙集理论和Vague集理论相结合，引入了粗糙Vague集的基本概念。利用VOIS中的粗糙模糊下近似分布，引入属性约简的概念。然后，我们开发了一个基于区分矩阵的算法来计算所有的属性约简。最后，提出了一种从VOIS中提取决策规则的可行方法。文中还给出了一个实例，说明了所提出的理论和方法在医疗诊断问题中的应用。
Abstract: The growing volume of vague information poses interesting challenges and calls for new theories, techniques and tools for analysis of vague data sets. In this paper, we study how to extract knowledge from vague objective information systems (VOISs) based on rough sets theory. We first introduce the basic notion termed rough vague sets by combining rough sets theory and vague sets theory. By using the rough vague lower approximation distribution in the VOIS, the concept of attribute reduction is introduced. Then, we develop an algorithm based on a discernibility matrix to compute all the attribute reductions. Finally, a viable approach for extracting decision rules from the VOIS is proposed. An example is also presented to illustrate the application of the proposed theories and approaches in handling medical diagnosis problems.