Evidence-theory-based numerical characterization of multigranulation rough sets in incomplete information systems

详细信息    查看全文

• 作者：Anhui Tan^a ; ^b ; ^{tananhui86@163.com" class="auth_mail" title="E-mail the corresponding author} ; Weizhi Wu^a ; ^b ; ^{wuwz@zjou.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Jinjin Li^c ; ^{jinjinli@mnnu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Guoping Lin^c ; ^{guoplin@163.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Attribute reduction ; Belief function ; Evidence theory ; Incomplete information system ; Multigranulation rough set ; Plausibility function
• 刊名：Fuzzy Sets and Systems
• 出版年：2016
• 出版时间：1 July 2016
• 年：2016
• 卷：294
• 期：Complete
• 页码：18-35
• 全文大小：381 K

文摘

Multigranulation rough sets are desirable features in the field of rough set, where this concept is approximated by multiple granular structures. In this study, we employ belief and plausibility functions from evidence theory to characterize the set approximations and attribute reductions in multigranulation rough set theory. First, we show that in an incomplete information system, the pessimistic multigranulation approximations can be measured by belief and plausibility functions, whereas the optimistic multigranulation approximations do not possess this characteristic in general. We also give a sufficient and necessary condition for the numerical measurement of optimistic multigranulation approximations by belief and plausibility functions. Second, in an incomplete decision system, the pessimistic multigranulation approximations are also measured by belief and plausibility functions. In the end, an attribute reduction algorithm for multigranulation rough sets is proposed based on evidence theory, and its efficiency is examined by an example. Thus, belief and plausibility functions can be employed to numerically characterize the attribute reductions and to construct an attribute reduction algorithm for multigranulation rough sets.