Paper Info
Title
Rules Induced from Rough Sets in Information Tables with Continuous Values.
Abstract
Rule induction based on neighborhood rough sets is described in information tables with continuous values. An indiscernible range that a value has in an attribute is determined by a threshold on that attribute. The indiscernibility relation is derived from using the indiscernible range. First, lower and upper approximations are described in complete information tables by directly using the indiscernibility relation. Rules are obtained from the approximations. To improve the applicability of rules, a series of rules is put into one rule expressed with an interval value, which is called a combined rule. Second, these are addressed in incomplete information tables. Incomplete information is expressed by a set of values or an interval value. The indiscernibility relations are constructed from two viewpoints: certainty and possibility. Consequently, we obtain four types of approximations: certain lower, certain upper, possible lower, and possible upper approximations. Using these approximations, rough sets are expressed by interval sets. From these approximations we obtain four types of combined rules: certain and consistent, certain and inconsistent, possible and consistent, and possible and inconsistent ones. These combined rules have greater applicability than single rules that individual objects support.
Year
DOI
Venue
2018
10.1007/978-3-319-91476-3_41
Communications in Computer and Information Science
Keywords
Field
DocType
Neighborhood rough sets,Rule induction,Incomplete information,Indiscernibility relation,Lower and upper approximations,Continuous values
Certainty,Algorithm,Rough set,Rule induction,Complete information,Mathematics
Conference
Volume
ISSN
Citations 
854
1865-0929
0
PageRank 
References 
Authors
0.34
12
3
Name
Order
Citations
PageRank
Michinori Nakata129237.49
Hiroshi Sakai210716.41
Keitarou Hara3315.55