Author Type

Graduate Student

Date of Award

2004

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Khoshgoftaar, Taghi M.

Abstract

This thesis presents two new noise filtering techniques which improve the quality of training datasets by removing noisy data. The training dataset is first split into subsets, and base learners are induced on each of these splits. The predictions are combined in such a way that an instance is identified as noisy if it is misclassified by a certain number of base learners. The Multiple-Partitioning Filter combines several classifiers on each split. The Iterative-Partitioning Filter only uses one base learner, but goes through multiple iterations. The amount of noise removed is varied by tuning the filtering level or the number of iterations. Empirical studies on a high assurance software project compare the effectiveness of our noise removal approaches with two other filters, the Cross-Validation Filter and the Ensemble Filter. Our studies suggest that using several base classifiers as well as performing several iterations with a conservative scheme may improve the efficiency of the filter.

Extent

257 p.

Comments

College of Engineering and Computer Science|FAU Electronic Theses and Dissertations Collection|Thesis (M.S.)--Florida Atlantic University, 2004.|College of Engineering and Computer Science

Identifier

FADT13110

Format

pdf

Download

Share

COinS