A GA-based approach for mining membership functions and concept-drift patterns

機構典藏 > College of Engineering > Graduate Institute & Department of Computer Science and Information Engineering > Proceeding > Item 987654321/106387

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/106387

Title:	A GA-based approach for mining membership functions and concept-drift patterns
Authors:	Chen, C. H.;Li, Y.;Hong, T. P.;Li, Y. K.;Lu, E. H. C.
Keywords:	concept drift;data mining;fuzzy association rules;genetic algorithms;membership functions
Date:	2015-05-25
Issue Date:	2016-04-27 11:11:30 (UTC+8)
Publisher:	IEEE
Abstract:	Since customers' behaviors may change over time in real applications, algorithms that can be utilized to mine these drift patterns are needed. In this paper, we propose a GA-based approach for mining fuzzy concept-drift patterns. It consists of two phases. The first phase mines membership functions and the second one finds fuzzy concept-drift patterns. In the first phase, appropriate membership functions for items are derived by GA with a designed fitness function. Then, the derived membership functions are utilized to mine fuzzy concept-drift patterns in the second phase. Experiments on simulated datasets are also made to show the effectiveness of the proposed approach.
Relation:	Evolutionary Computation (CEC), 2015 IEEE, pp.2961-2965
DOI:	10.1109/CEC.2015.7257257
Appears in Collections:	[Graduate Institute & Department of Computer Science and Information Engineering] Proceeding

Files in This Item:

File	Description	Size	Format
DetailProgram-ver19.pdf	議程	651Kb	Adobe PDF	247	View/Open
index.html		0Kb	HTML	186	View/Open