A granular computing approach to improve large attributes learning

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Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8872

Title:	A granular computing approach to improve large attributes learning
Authors:	Chang, Fengming M.;Chan, Chien-Chung
Contributors:	Department of Information Science and Applications
Keywords:	Artificial intelligence;Bayesian networks;Decision trees;Granular computing;Inference engines;Support vector machines;Artificial Neural Network;Data attributes;Data sets;Learning accuracy;Learning methods;Machine-learning;Mega-fuzzification;Neuro-Fuzzy
Date:	2009
Issue Date:	2010-04-08 12:35:57 (UTC+0)
Publisher:	Asia University
Abstract:	Based on the concept of granular computing, this article proposes a novel Boolean Conversion (BC) method to reduce data attribute number for the purpose of improving the efficiency of learning in artificial intelligence. Data with large amount of attributes usually cause a system freezes or shuts down. The proposed method combines large amount attributes to smaller number ones by the way of Boolean method. Three data sets are used to compare the learning accuracies and efficiencies by Bayesian networks (BN), C4.5 decision tree, support vector machine (SVM), artificial neural network (ANN), fuzzy neural network (FNN, neuro-fuzzy), and Mega-fuzzification learning methods. Results indicate that the proposed BC method can improve the efficiency of machine learning and the accuracy is not worse. ©2009 IEEE.
Relation:	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics :2521-2525
Appears in Collections:	[行動商務與多媒體應用學系] 會議論文

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