ACM international workshop on multimedia technologies for distance learning (MTDL 2009)

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Accession number:20100412664599

Title: A granular computing approach to improve large attributes learning

Authors: Chang, Fengming M. (1); Chan, Chien-Chung (2) Author affiliation:(1) Department of Information Science and

Applications, Asia University, Wufeng, Taichung 41354, Taiwan; (2) Department of Computer Science, University of Akron, Akron, OH 44325-4003, United States

Corresponding author:Chang, F. M.


Source title: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Abbreviated source title:Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.

Monograph title:Proceedings 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009

Issue date:2009

Publication year:2009 Pages:2521-2525

Article number:5346332 Language:English



Document type:Conference article (CA)

Conference name:2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009

Conference date:October 11, 2009 - October 14, 2009 Conference location:San Antonio, TX, United states Conference code:79080

Sponsor:IEEE Systems, Man, and Cybernetics Society

Publisher:Institute of Electrical and Electronics Engineers Inc., 3 Park Avenue, 17th Floor, New York, NY 10016-5997, United States

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.

Number of references:24

Main heading:Fuzzy neural networks

Controlled terms: Artificial intelligence - Bayesian networks - Decision trees - Granular computing - Inference engines - Support vector machines

Uncontrolled terms: Artificial Neural Network - Data attributes - Data sets - Learning accuracy - Learning methods - Machine-learning - Mega-fuzzification - Neuro-Fuzzy

Classification code:922 Statistical Methods - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory - 912.1 Industrial Engineering - 961 Systems Science - 723.4.1 Expert Systems - 723.2 Data Processing and Image Processing - 723 Computer Software, Data Handling and Applications - 723.4 Artificial Intelligence DOI:10.1109/ICSMC.2009.5346332


Compilation and indexing terms, Copyright 2009 Elsevier Inc.




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