NDLI: A granular computing approach to improve large attributes learning

Content Provider	IEEE Xplore Digital Library
Author	Chang, F.M. Chien-Chung Chan
Copyright Year	2009
Description	Author affiliation: Department of Information Science and Applications, Asia University, Wufeng, Taichung 41354, Taiwan (Chang, F.M.) \|\| Department of Computer Science, University of Akron, Akron, OH 44325-4003, USA (Chien-Chung Chan)
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.
Starting Page	2521
Ending Page	2525
File Size	163187
Page Count	5
File Format	PDF
ISBN	9781424427932
ISSN	1062922X
DOI	10.1109/ICSMC.2009.5346332
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy neural networks Artificial intelligence Artificial neural networks Support vector machines Learning systems Computer networks Machine learning Costs Fuzzy systems Bayesian methods
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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