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