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政大典藏 > College of Informatics > Department of Computer Science > Periodical Articles > Item 140.119/76055

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/76055

Title:	Feature selection using genetic algorithm and cluster validation
Authors:	Wu, Y.-L.;Tang, C.-Y.;Hor, Maw-Kae;Wu, P.-F. 何瑁鎧
Contributors:	資科系
Keywords:	Cluster validation;Convergence time;Feature selection;Feature sets;Image retrieval systems;Low-level image features;Retrieval accuracy;Taguchi;Two directions;Clustering algorithms;Genetic algorithms;Image retrieval;Taguchi methods;Feature extraction
Date:	2011-03
Issue Date:	2015-06-22 16:13:39 (UTC+8)
Abstract:	Feature selection plays an important role in image retrieval systems. The better selection of features usually results in higher retrieval accuracy. This work tries to select the best feature set from a total of 78 low level image features, including regional, color, and textual features, using the genetic algorithms (GA). However, the GA is known to be slow to converge. In this work we propose two directions to improve the convergence time of the GA. First we employ the Taguchi method to reduce the number of necessary offspring to be tested in every generation in the GA. Second we propose to use an alternative measure, the Hubert`s Γ statistics, to evaluate the fitness of each offspring instead of evaluating the retrieval accuracy directly. The experiment results show that the proposed techniques improve the feature selection results by using the GA in both time and accuracy. © 2010 Elsevier Ltd. All rights reserved.
Relation:	Expert Systems with Applications, 38(3), 2727-2732
Data Type:	article
DOI link:	http://dx.doi.org/10.1016/j.eswa.2010.08.062
DOI:	10.1016/j.eswa.2010.08.062
Appears in Collections:	[Department of Computer Science ] Periodical Articles

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