research-article

Support vector machines for spam categorization

Authors:

H. Drucker,

Donghui Wu,

V. N. VapnikAuthors Info & Claims

IEEE Transactions on Neural Networks, Volume 10, Issue 5

Pages 1048 - 1054

https://doi.org/10.1109/72.788645

Published: 01 September 1999 Publication History

Abstract

We study the use of support vector machines (SVM) in classifying e-mail as spam or nonspam by comparing it to three other classification algorithms: Ripper, Rocchio, and boosting decision trees. These four algorithms were tested on two different data sets: one data set where the number of features were constrained to the 1000 best features and another data set where the dimensionality was over 7000. SVM performed best when using binary features. For both data sets, boosting trees and SVM had acceptable test performance in terms of accuracy and speed. However, SVM had significantly less training time

Cited By

View all

Olivo CSantin AViegas EGeremias JSouto E(2025)Towards a reliable spam detection: an ensemble classification with rejection optionCluster Computing10.1007/s10586-024-04742-728:1Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s10586-024-04742-7
Zhou JChen YJin XMao WXiao ZZhang SZhang TLiu TKendrick KJiang X(2024)Fusing multi-scale functional connectivity patterns via Multi-Branch Vision Transformer (MB-ViT) for macaque brain age predictionNeural Networks10.1016/j.neunet.2024.106592179:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106592
Shrivastava SShukla SKhare N(2024)Support vector machine with eagle loss functionExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122168238:PEOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122168
Show More Cited By

Support vector machines for spam categorization
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
2. Information systems
  1. World Wide Web
    1. Web applications
      1. Internet communications tools

Recommendations

Wavelet twin support vector machines based on glowworm swarm optimization

Twin support vector machine is a machine learning algorithm developing from standard support vector machine. The performance of twin support vector machine is always better than support vector machine on datasets that have cross regions. Recently ...
PAC-Bayes bounds for twin support vector machines

Twin support vector machines are regarded as a milestone in the development of support vector machines. Compared to standard support vector machines, they learn two nonparallel hyperplanes rather than one as in standard support vector machines for ...
Multitask centroid twin support vector machines

Twin support vector machines are a recently proposed learning method for binary classification. They learn two hyperplanes rather than one as in conventional support vector machines and often bring performance improvements. However, an inherent shortage ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image IEEE Transactions on Neural Networks

IEEE Transactions on Neural Networks Volume 10, Issue 5

September 1999

275 pages

ISSN:1045-9227

Issue’s Table of Contents

Publisher

IEEE Press

Publication History

Published: 01 September 1999

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

277
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Olivo CSantin AViegas EGeremias JSouto E(2025)Towards a reliable spam detection: an ensemble classification with rejection optionCluster Computing10.1007/s10586-024-04742-728:1Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s10586-024-04742-7
Zhou JChen YJin XMao WXiao ZZhang SZhang TLiu TKendrick KJiang X(2024)Fusing multi-scale functional connectivity patterns via Multi-Branch Vision Transformer (MB-ViT) for macaque brain age predictionNeural Networks10.1016/j.neunet.2024.106592179:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106592
Shrivastava SShukla SKhare N(2024)Support vector machine with eagle loss functionExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122168238:PEOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122168
Emam MHoussein ESamee NAlkhalifa AHosney M(2024)Optimizing cancer diagnosisComputers in Biology and Medicine10.1016/j.compbiomed.2024.108984180:COnline publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108984
Mishra UGupta DHazarika B(2024)An efficient angle-based twin random vector functional link classifierApplied Soft Computing10.1016/j.asoc.2024.112005164:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.112005
Zhang WGu W(2024)Machine learning for a class of partial differential equations with multi-delays based on numerical Gaussian processesApplied Mathematics and Computation10.1016/j.amc.2023.128498467:COnline publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1016/j.amc.2023.128498
Shrivastava SShukla SKhare N(2023)A stable variant of linex loss SVM for handling noise with reduced hyperparametersInformation Sciences: an International Journal10.1016/j.ins.2023.119402646:COnline publication date: 29-Aug-2023
https://dl.acm.org/doi/10.1016/j.ins.2023.119402
Zavrak SYilmaz S(2023)Email spam detection using hierarchical attention hybrid deep learning methodExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120977233:COnline publication date: 15-Dec-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120977
Osama SShaban HAli A(2023)Gene reduction and machine learning algorithms for cancer classification based on microarray gene expression dataExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118946213:PAOnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.118946
Charanarur PJain HRao GSamanta DSengar SHewage C(2023)Machine-Learning-Based Spam Mail DetectorSN Computer Science10.1007/s42979-023-02330-x4:6Online publication date: 8-Nov-2023
https://dl.acm.org/doi/10.1007/s42979-023-02330-x
Show More Cited By

Abstract

Cited By

Recommendations

Wavelet twin support vector machines based on glowworm swarm optimization

PAC-Bayes bounds for twin support vector machines

Multitask centroid twin support vector machines

Comments

Information

Published In

Publisher

Publication History

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations