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Correcting noisy data and expert analysis of the correction process

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Date Issued:
2005
Summary:
This thesis expands upon an existing noise cleansing technique, polishing, enabling it to be used in the Software Quality Prediction domain, as well as any other domain where the data contains continuous values, as opposed to categorical data for which the technique was originally designed. The procedure is applied to a real world dataset with real (as opposed to injected) noise as determined by an expert in the domain. This, in combination with expert assessment of the changes made to the data, provides not only a more realistic dataset than one in which the noise (or even the entire dataset) is artificial, but also a better understanding of whether the procedure is successful in cleansing the data. Lastly, this thesis provides a more in-depth view of the process than previously available, in that it gives results for different parameters and classifier building techniques. This allows the reader to gain a better understanding of the significance of both model generation and parameter selection.
Title: Correcting noisy data and expert analysis of the correction process.
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Name(s): Seiffert, Christopher N.
Florida Atlantic University, Degree grantor
Khoshgoftaar, Taghi M., Thesis advisor
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 2005
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 95 p.
Language(s): English
Summary: This thesis expands upon an existing noise cleansing technique, polishing, enabling it to be used in the Software Quality Prediction domain, as well as any other domain where the data contains continuous values, as opposed to categorical data for which the technique was originally designed. The procedure is applied to a real world dataset with real (as opposed to injected) noise as determined by an expert in the domain. This, in combination with expert assessment of the changes made to the data, provides not only a more realistic dataset than one in which the noise (or even the entire dataset) is artificial, but also a better understanding of whether the procedure is successful in cleansing the data. Lastly, this thesis provides a more in-depth view of the process than previously available, in that it gives results for different parameters and classifier building techniques. This allows the reader to gain a better understanding of the significance of both model generation and parameter selection.
Identifier: 9780496984633 (isbn), 13223 (digitool), FADT13223 (IID), fau:10080 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (M.S.)--Florida Atlantic University, 2005.
Subject(s): Computer interfaces--Software--Quality control
Acoustical engineering
Noise control--Computer programs
Expert systems (Computer science)
Software documentation
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/13223
Sublocation: Digital Library
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.