Feature selection and approximate reasoning of large-scale set-valued decision tables based on α-dominance-based quantitative rough sets

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• 作者：Hong-Ying Zhang ; ^{zhyemily@mail.xjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Shu-Yun Yang ^{yangshuyun2009125@163.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Set-valued decision tables ; α-dominance relation ; Quantitative rough sets ; Feature selection ; Approximate reasoning ; Disjunctive ; Conjunctive
• 刊名：Information Sciences
• 出版年：2017
• 出版时间：1 February 2017
• 年：2017
• 卷：378
• 期：Complete
• 页码：328-347
• 全文大小：770 K

文摘

Set-valued data are a common type of data for characterizing uncertain and missing information. Traditional dominance-based rough sets can not efficiently deal with large-scale set-valued decision tables and usually neglect the disjunctive semantics of sets. In this paper, we propose a general framework of feature selection and approximate reasoning for large-scale set-valued information tables by integrating quantitative rough sets and dominance-based rough sets. Firstly, we define two new partial orders for set-valued data via the conjunctive and disjunctive semantics of a set. Secondly, based on α-disjunctive dominance relation and α-conjunctive dominance relation defined by the inclusion measure, we present α-dominance-based quantitative rough set models for these two types of set-valued decision tables. Furthermore, we study the issue of feature selection in set-valued decision tables by employing α-dominance-based quantitative rough set models and discuss the relationships between the relative reductions and discernibility matrices. We also present approximate reasoning models based on α-dominance-based quantitative rough sets. Finally, the application of the approach is illustrated by some real-world data sets.