Date of Award
2015
Document Type
Dissertation
Publication Status
Version of Record
Submission Date
October 2026
Thesis/Dissertation Advisor [Chair]
Khoshgoftaar, Taghi M.
Abstract
In response to the massive amounts of data that make up a large number of bioinformatics datasets, it has become increasingly necessary for researchers to use computers to aid them in their endeavors. With difficulties such as high dimensionality, class imbalance, noisy data, and difficult to learn class boundaries, being present within the data, bioinformatics datasets are a challenge to work with. One potential source of assistance is the domain of data mining and machine learning, a field which focuses on working with these large amounts of data and develops techniques to discover new trends and patterns that are hidden within the data and to increases the capability of researchers and practitioners to work with this data. Within this domain there are techniques designed to eliminate irrelevant or redundant features, balance the membership of the classes, handle errors found in the data, and build predictive models for future data.
Extent
157 p.
Recommended Citation
Dittman, David, "Machine learning techniques for alleviating inherent difficulties in bioinformatics data" (2015). Electronic Theses and Dissertations 2010 - 2019. 1258.
https://digitalcommons.fau.edu/etd_general_6/1258
Identifier
FA00004362
Comments
Includes bibliography.|Dissertation (Ph.D.)--Florida Atlantic University, 2015.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.