relation: https://khub.utp.edu.my/scholars/10335/
title: Knowledge acquisition from rough sets using merged decision rules
creator: Matsumoto, Y.
creator: Watada, J.
description: 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 yen�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.
publisher: Fuji Technology Press
date: 2018
type: Article
type: PeerReviewed
identifier:   Matsumoto, Y. and Watada, J.  (2018) Knowledge acquisition from rough sets using merged decision rules.  Journal of Advanced Computational Intelligence and Intelligent Informatics, 22 (3).  pp. 404-410.  ISSN 13430130     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047771875&doi=10.20965%2fjaciii.2018.p0404&partnerID=40&md5=e6d3dac328938004f4df4fb11508ff1f
relation: 10.20965/jaciii.2018.p0404
identifier: 10.20965/jaciii.2018.p0404