NDLI: A Distributed Hierarchical Graph Neuron-Based Classifier: An Efficient, Low-Computational Classifier

Content Provider	IEEE Xplore Digital Library
Author	Raja Mahmood, R.A. Muhamad Amin, A.H. Khan, A.I.
Copyright Year	2008
Description	Author affiliation: Clayton Sch. of IT, Monash Univ. Victoria, Clayton, VIC (Raja Mahmood, R.A.; Muhamad Amin, A.H.; Khan, A.I.)
Abstract	Many of the widely used classifiers are time consuming and resource intensive, and hence not practical to be used in the emerging wireless networks. We present an efficient classifier, termed distributed hierarchical graph neuron (DHGN)-based classifier. Our proposed solution uses a new form of neural network, which consists of a hierarchical graph-based representation of input patterns, and adopts a one-cycle learning process. We compare the effectiveness and computational complexity of our proposed classifier with the well known self-organizing map (SOM) classifier in a supervised environment. The results show that the DHGN-based classifier offers lower computational complexity than SOM while guaranteeing satisfactory classification accuracy.
Starting Page	675
Ending Page	680
File Size	277729
Page Count	6
File Format	PDF
ISBN	9780769533919
DOI	10.1109/ICINIS.2008.161
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Self-Organizing Map Neurons Power system security classification Computational complexity Distributed Hierarchical Graph Neuron-based classifier Support vector machines Intelligent networks Wireless networks Neural networks Support vector machine classification Intelligent systems Classification tree analysis computational complexity
Content Type	Text
Resource Type	Article

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