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Automatic generation of meta classifiers with large levels for distributed computing and networking

Abawajy,J, Kelarev,A and Chowdhury,M 2014, Automatic generation of meta classifiers with large levels for distributed computing and networking, Journal of Networks, vol. 9, no. 9, pp. 2259-2268, doi: 10.4304/jnw.9.9.2259-2268.

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Title Automatic generation of meta classifiers with large levels for distributed computing and networking
Author(s) Abawajy,J
Kelarev,A
Chowdhury,MORCID iD for Chowdhury,M orcid.org/0000-0002-2866-4955
Journal name Journal of Networks
Volume number 9
Issue number 9
Start page 2259
End page 2268
Total pages 10
Publisher Academy Publisher
Place of publication Oulu, Finland
Publication date 2014-09
ISSN 1796-2056
Keyword(s) distributed meta classifiers
networking meta classifiers
automatically generated meta classifiers with large levels
SMO
communication security applications
Summary This paper is devoted to a case study of a new construction of classifiers. These classifiers are called automatically generated multi-level meta classifiers, AGMLMC. The construction combines diverse meta classifiers in a new way to create a unified system. This original construction can be generated automatically producing classifiers with large levels. Different meta classifiers are incorporated as low-level integral parts of another meta classifier at the top level. It is intended for the distributed computing and networking. The AGMLMC classifiers are unified classifiers with many parts that can operate in parallel. This make it easy to adopt them in distributed applications. This paper introduces new construction of classifiers and undertakes an experimental study of their performance. We look at a case study of their effectiveness in the special case of the detection and filtering of phishing emails. This is a possible important application area for such large and distributed classification systems. Our experiments investigate the effectiveness of combining diverse meta classifiers into one AGMLMC classifier in the case study of detection and filtering of phishing emails. The results show that new classifiers with large levels achieved better performance compared to the base classifiers and simple meta classifiers classifiers. This demonstrates that the new technique can be applied to increase the performance if diverse meta classifiers are included in the system.
Language eng
DOI 10.4304/jnw.9.9.2259-2268
Field of Research 080501 Distributed and Grid Systems
080503 Networking and Communications
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Academy Publisher
Persistent URL http://hdl.handle.net/10536/DRO/DU:30071502

Document type: Journal Article
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.