Author Type

Graduate Student

Date of Award

2008

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Khoshgoftaar, Taghi M.

Abstract

A variety of classifiers for solving classification problems is available from the domain of machine learning. Commonly used classifiers include support vector machines, decision trees and neural networks. These classifiers can be configured by modifying internal parameters. The large number of available classifiers and the different configuration possibilities result in a large number of combinatiorrs of classifier and configuration settings, leaving the practitioner with the problem of evaluating the performance of different classifiers. This problem can be solved by using performance metrics. However, the large number of available metrics causes difficulty in deciding which metrics to use and when comparing classifiers on the basis of multiple metrics. This paper uses the statistical method of factor analysis in order to investigate the relationships between several performance metrics and introduces the concept of relative performance which has the potential to case the process of comparing several classifiers. The relative performance metric is also used to evaluate different support vector machine classifiers and to determine if the default settings in the Weka data mining tool are reasonable.

Extent

164 p.

Comments

College of Engineering and Computer Science|Thesis (M.S.)--Florida Atlantic University, 2008.|FAU Electronic Theses and Dissertations Collection|College of Engineering and Computer Science

Identifier

FA00012508

Format

pdf

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