On rule acquisition in incomplete multi-scale decision tables

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• 作者：Wei-Zhi Wu ; ^a ; ^b ; ^{wuwz@zjou.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; ^{wuwz8681@sina.com" class="auth_mail" title="E-mail the corresponding author} ; Yuhua Qian^c ; ^{jinchengqyh@126.com" class="auth_mail" title="E-mail the corresponding author} ; Tong-Jun Li^a ; ^b ; ^{litj@zjou.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Shen-Ming Gu^a ; ^b ; ^{gsm@zjou.edu.cn" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Belief functions ; Granular computing ; Incomplete information tables ; Multi-scale decision tables ; Rough sets
• 刊名：Information Sciences
• 出版年：2017
• 出版时间：1 February 2017
• 年：2017
• 卷：378
• 期：Complete
• 页码：282-302
• 全文大小：682 K

文摘

Granular computing and acquisition of IF-THEN rules are two basic issues in knowledge representation and data mining. A rough set approach to knowledge discovery in incomplete multi-scale decision tables from the perspective of granular computing is proposed in this paper. The concept of incomplete multi-scale information tables in the context of rough sets is first introduced. Information granules at different levels of scales in incomplete multi-scale information tables are then described. Lower and upper approximations with reference to different levels of scales in incomplete multi-scale information tables are also defined and their properties are examined. Optimal scale selection with various requirements in incomplete multi-scale decision tables are further discussed. Relationships among different notions of optimal scales in incomplete multi-scale decision tables are presented. Finally, knowledge acquisition in the sense of rule induction in consistent and inconsistent incomplete multi-scale decision tables are explored.