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

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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
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
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Index Terms

Support vector machines for spam categorization
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
2. Information systems
  1. World Wide Web
    1. Web applications
      1. Internet communications tools
        Email

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Neural Networks

IEEE Transactions on Neural Networks Volume 10, Issue 5

September 1999

275 pages

ISSN:1045-9227

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IEEE Press

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Published: 01 September 1999

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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
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
Méndez JCotos-Yañez TRuano-Ordás D(2022)A new semantic-based feature selection method for spam filteringApplied Soft Computing10.1016/j.asoc.2018.12.00876:C(89-104)Online publication date: 19-Apr-2022
https://dl.acm.org/doi/10.1016/j.asoc.2018.12.008
Robinson KMago V(2022)Birds of prey: identifying lexical irregularities in spam on TwitterWireless Networks10.1007/s11276-018-01900-928:3(1189-1196)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s11276-018-01900-9
Mishra UGupta DHazarika B(2022)An Intuitionistic Fuzzy Random Vector Functional Link ClassifierNeural Processing Letters10.1007/s11063-022-11043-w55:4(4325-4346)Online publication date: 22-Oct-2022
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Hazarika BGupta D(2022)Density Weighted Twin Support Vector Machines for Binary Class Imbalance LearningNeural Processing Letters10.1007/s11063-021-10671-y54:2(1091-1130)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s11063-021-10671-y
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Wavelet twin support vector machines based on glowworm swarm optimization

PAC-Bayes bounds for twin support vector machines

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