Author Type

Graduate Student

Date of Award

2006

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Khoshgoftaar, Taghi M.

Abstract

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.

Extent

64 p.

Comments

College of Engineering and Computer Science|Thesis (M.S.)--Florida Atlantic University, 2006.|FAU Electronic Theses and Dissertations Collection|College of Engineering and Computer Science

Identifier

FA00012521

Format

pdf

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