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An Empirical Study of Ordinal and Non-ordinal Classification Algorithms for Intrusion Detection in WLANs

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Date Issued:
2006
Abstract/Description:
Ordinal classification refers to an important category of real world problems, in which the attributes of the instances to be classified and the classes are linearly ordered. Many applications of machine learning frequently involve situations exhibiting an order among the different categories represented by the class attribute. In ordinal classification the class value is converted into a numeric quantity and regression algorithms are applied to the transformed data. The data is later translated back into a discrete class value in a postprocessing step. This thesis is devoted to an empirical study of ordinal and non-ordinal classification algorithms for intrusion detection in WLANs. We used ordinal classification in conjunction with nine classifiers for the experiments in this thesis. All classifiers are parts of the WEKA machinelearning workbench. The results indicate that most of the classifiers give similar or better results with ordinal classification compared to non-ordinal classification.
Title: An Empirical Study of Ordinal and Non-ordinal Classification Algorithms for Intrusion Detection in WLANs.
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Name(s): Gopalakrishnan, Leelakrishnan
Khoshgoftaar, Taghi M., Thesis advisor
Florida Atlantic University, Degree grantor
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2006
Date Issued: 2006
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 64 p.
Language(s): English
Abstract/Description: Ordinal classification refers to an important category of real world problems, in which the attributes of the instances to be classified and the classes are linearly ordered. Many applications of machine learning frequently involve situations exhibiting an order among the different categories represented by the class attribute. In ordinal classification the class value is converted into a numeric quantity and regression algorithms are applied to the transformed data. The data is later translated back into a discrete class value in a postprocessing step. This thesis is devoted to an empirical study of ordinal and non-ordinal classification algorithms for intrusion detection in WLANs. We used ordinal classification in conjunction with nine classifiers for the experiments in this thesis. All classifiers are parts of the WEKA machinelearning workbench. The results indicate that most of the classifiers give similar or better results with ordinal classification compared to non-ordinal classification.
Identifier: FA00012521 (IID)
Degree granted: Thesis (M.S.)--Florida Atlantic University, 2006.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Subject(s): Wireless LANs--Security measures
Computer networks--Security measures
Data structures (Computer science)
Multivariate analysis
Held by: Florida Atlantic University Libraries
Sublocation: Digital Library
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00012521
Use and Reproduction: Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.