Author Type

Graduate Student

Date of Award

2007

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Khoshgoftaar, Taghi M.

Abstract

Class imbalance tends to cause inferior performance in data mining learners, particularly with regard to predicting the minority class, which generally imposes a higher misclassification cost. This work explores the benefits of using genetic algorithms (GA) to develop classification models which are better able to deal with the problems encountered when mining datasets which suffer from class imbalance. Using GA we evolve configuration parameters suited for skewed datasets for three different learners: artificial neural networks, 0 4.5 decision trees, and RIPPER. We also propose a novel technique called evolutionary sampling which works to remove noisy and unnecessary duplicate instances so that the sampled training data will produce a superior classifier for the imbalanced dataset. Our GA fitness function uses metrics appropriate for dealing with class imbalance, in particular the area under the ROC curve. We perform extensive empirical testing on these techniques and compare the results with seven exist ing sampling methods.

Extent

172 p.

Comments

College of Engineering and Computer Science|Thesis (M.S.)--Florida Atlantic University, 2007.|FAU Electronic Theses and Dissertations Collection|College of Engineering and Computer Science

Identifier

FA00012515

Format

pdf

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