Rohitha K K, Hewawasam G K, Premaratne Kamal, Shyu Mei-Ling
Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33124, USA.
IEEE Trans Syst Man Cybern B Cybern. 2007 Dec;37(6):1446-59. doi: 10.1109/tsmcb.2007.903536.
Management of data imprecision and uncertainty has become increasingly important, especially in situation awareness and assessment applications where reliability of the decision-making process is critical (e.g., in military battlefields). These applications require the following: 1) an effective methodology for modeling data imperfections and 2) procedures for enabling knowledge discovery and quantifying and propagating partial or incomplete knowledge throughout the decision-making process. In this paper, using a Dempster-Shafer belief-theoretic relational database (DS-DB) that can conveniently represent a wider class of data imperfections, an association rule mining (ARM)-based classification algorithm possessing the desirable functionality is proposed. For this purpose, various ARM-related notions are revisited so that they could be applied in the presence of data imperfections. A data structure called belief itemset tree is used to efficiently extract frequent itemsets and generate association rules from the proposed DS-DB. This set of rules is used as the basis on which an unknown data record, whose attributes are represented via belief functions, is classified. These algorithms are validated on a simplified situation assessment scenario where sensor observations may have caused data imperfections in both attribute values and class labels.
数据不精确性和不确定性的管理变得越来越重要，特别是在态势感知和评估应用中，决策过程的可靠性至关重要（例如在军事战场中）。这些应用需要以下两点：1）一种用于对数据缺陷进行建模的有效方法；2）在整个决策过程中实现知识发现、量化和传播部分或不完整知识的程序。在本文中，使用一种能方便地表示更广泛数据缺陷类别的Dempster-Shafer信念理论关系数据库（DS-DB），提出了一种具有理想功能的基于关联规则挖掘（ARM）的分类算法。为此，重新审视了各种与ARM相关的概念，以便它们能在存在数据缺陷的情况下应用。一种称为信念项集树的数据结构用于从所提出的DS-DB中高效提取频繁项集并生成关联规则。这组规则用作对未知数据记录进行分类的基础，该未知数据记录的属性通过信念函数表示。这些算法在一个简化的态势评估场景中得到验证，在该场景中传感器观测可能在属性值和类别标签两方面都导致了数据缺陷。
