You are not logged in.

Large iterative multitier ensemble classifiers for security of big data

Abawajy,JH, Kelarev,A and Chowdhury,M 2014, Large iterative multitier ensemble classifiers for security of big data, IEEE transactions on emerging topics in computing, vol. 2, no. 3, pp. 352-363, doi: 10.1109/TETC.2014.2316510.

Attached Files
Name Description MIMEType Size Downloads

Title Large iterative multitier ensemble classifiers for security of big data
Author(s) Abawajy,JH
Kelarev,A
Chowdhury,MORCID iD for Chowdhury,M orcid.org/0000-0002-2866-4955
Journal name IEEE transactions on emerging topics in computing
Volume number 2
Issue number 3
Start page 352
End page 363
Publisher IEEE
Place of publication New York, NY
Publication date 2014-10-30
ISSN 2168-6750
Keyword(s) big data
ensemble meta classifiers
LIME classifiers
random forest
Summary This paper introduces and investigates large iterative multitier ensemble (LIME) classifiers specifically tailored for big data. These classifiers are very large, but are quite easy to generate and use. They can be so large that it makes sense to use them only for big data. They are generated automatically as a result of several iterations in applying ensemble meta classifiers. They incorporate diverse ensemble meta classifiers into several tiers simultaneously and combine them into one automatically generated iterative system so that many ensemble meta classifiers function as integral parts of other ensemble meta classifiers at higher tiers. In this paper, we carry out a comprehensive investigation of the performance of LIME classifiers for a problem concerning security of big data. Our experiments compare LIME classifiers with various base classifiers and standard ordinary ensemble meta classifiers. The results obtained demonstrate that LIME classifiers can significantly increase the accuracy of classifications. LIME classifiers performed better than the base classifiers and standard ensemble meta classifiers.
Language eng
DOI 10.1109/TETC.2014.2316510
Field of Research 080501 Distributed and Grid Systems
Socio Economic Objective 890199 Communication Networks and Services not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30069165

Document type: Journal Article
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 6 times in TR Web of Science
Scopus Citation Count Cited 7 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 211 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Wed, 21 Jan 2015, 10:40:51 EST

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.