TY  - JOUR
PB  - Fuji Technology Press
SN  - 13430130
EP  - 410
AV  - none
N1  - cited By 1
SP  - 404
TI  - Knowledge acquisition from rough sets using merged decision rules
Y1  - 2018///
A1  - Matsumoto, Y.
A1  - Watada, J.
JF  - Journal of Advanced Computational Intelligence and Intelligent Informatics
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047771875&doi=10.20965%2fjaciii.2018.p0404&partnerID=40&md5=e6d3dac328938004f4df4fb11508ff1f
VL  - 22
N2  - Rough set theory was proposed by Z. Pawlak in 1982. This theory can mine knowledge based on a decision rule from a database, a web base, a set, and so on. The decision rule is used for data analysis as well as calculating an unknown object. We analyzed time-series data using rough sets. Economic time-series data was predicted using decision rules. However, there are cases where an excessive number of decision rules exist, from which, it is difficult to acquire knowledge. In this paper, we propose a method to reduce the number of decision rules by merging them. Similar to how it is difficult to acquire knowledge from multiple rules, it is also difficult to acquire knowledge from rules with a large number of condition attributes. We propose a method to reduce the number of condition attributes and thereby reduce the number of rules. We analyze time-series data using this proposed method and acquire knowledge for prediction using decision rules. We use TOPIX and the yenâ??dollar exchange rate as knowledge-acquisition data. We propose a method to facilitate knowledge acquisition by merging rules. © 2018 Fuji Technology Press. All Rights Reserved.
IS  - 3
ID  - scholars10335
KW  - Decision theory; Knowledge acquisition; Knowledge based systems; Knowledge management; Merging; Time series
KW  -  Condition attributes; Decision rules; Economic time series; Exchange rates; Knowledge based; Time-series data; Unknown objects
KW  -  Rough set theory
ER  -