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所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
关键字：Rough set Attribute reduction Dynamic data Incomplete decision system
摘要：Attribute reduction is an important process in data mining and knowledge discovery. In dynamic data environments, the attribute reduction problem has three issues: variation of object sets, variation of attribute sets and variation of attribute values. For the first two issues, a few achievements have been made. For variation of the attribute values, current attribute reduction approaches are not efficient, because the method becomes a non incremental or inefficient one in some cases. In order to address this, we first introduce the concept of an inconsistency degree in an incomplete decision system and prove that the attribute reduction based on the inconsistency degree is equivalent to that based on the positive region. Then, three update strategies of inconsistency degree for dynamic incomplete decision systems are provided. Finally, the framework of the incremental attribute reduction algorithm is proposed. Experiments on different data sets from UCI show the accuracy and feasibility of the proposed incremental reduction algorithms. (C) 2017 Elsevier Inc. All rights reserved.
ISSN号：0888-613X
是否译文：否
发表时间：2018-02-01
合写作者：Xie, Xiaojun
通讯作者：秦小麟