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DC Field | Value | Language |
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dc.contributor.author | Caruana, G | - |
dc.contributor.author | Li, M | - |
dc.contributor.author | Liu, Y | - |
dc.date.accessioned | 2014-07-28T15:30:39Z | - |
dc.date.available | 2014-07-28T15:30:39Z | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | Neurocomputing, 108, 45 - 57, 2013 | en_US |
dc.identifier.issn | 0925-2312 | - |
dc.identifier.uri | http://www.sciencedirect.com/science/article/pii/S0925231212008910 | en |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/8787 | - |
dc.description | This is the post-print version of the final paper published in Neurocomputing. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V. | en_US |
dc.description.abstract | Spam, under a variety of shapes and forms, continues to inflict increased damage. Varying approaches including Support Vector Machine (SVM) techniques have been proposed for spam filter training and classification. However, SVM training is a computationally intensive process. This paper presents a MapReduce based parallel SVM algorithm for scalable spam filter training. By distributing, processing and optimizing the subsets of the training data across multiple participating computer nodes, the parallel SVM reduces the training time significantly. Ontology semantics are employed to minimize the impact of accuracy degradation when distributing the training data among a number of SVM classifiers. Experimental results show that ontology based augmentation improves the accuracy level of the parallel SVM beyond the original sequential counterpart. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.subject | Spam filtering | en_US |
dc.subject | Support vector machine | en_US |
dc.subject | Parallel computing | en_US |
dc.subject | Classification | en_US |
dc.subject | MapReduce | en_US |
dc.title | An ontology enhanced parallel SVM for scalable spam filter training | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1016/j.neucom.2012.12.001 | - |
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pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences/Dept of Electronic and Computer Engineering | - |
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pubs.organisational-data | /Brunel/University Research Centres and Groups/Brunel Business School - URCs and Groups | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/Brunel Business School - URCs and Groups/Centre for Research into Entrepreneurship, International Business and Innovation in Emerging Markets | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups/Brunel Institute for Ageing Studies | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups/Brunel Institute of Cancer Genetics and Pharmacogenomics | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups/Centre for Systems and Synthetic Biology | - |
Appears in Collections: | Electronic and Electrical Engineering Dept of Electronic and Electrical Engineering Research Papers |
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Fulltext.pdf | 1.73 MB | Adobe PDF | View/Open |
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