Date of Award
2019
Document Type
Dissertation
Publication Status
Version of Record
Submission Date
October 2026
Thesis/Dissertation Advisor [Chair]
Khoshgoftaar, Taghi M.
Abstract
Recent technological developments have engendered an expeditious production of big data and also enabled machine learning algorithms to produce high-performance models from such data. Nonetheless, class imbalance (in binary classifications) between the majority and minority classes in big data can skew the predictive performance of the classification algorithms toward the majority (negative) class whereas the minority (positive) class usually holds greater value for the decision makers. Such bias may lead to adverse consequences, some of them even life-threatening, when the existence of false negatives is generally costlier than false positives. The size of the minority class can vary from fair to extraordinary small, which can lead to different performance scores for machine learning algorithms. Class imbalance is a well-studied area for traditional data, i.e., not big data. However, there is limited research focusing on both rarity and severe class imbalance in big data.
Extent
132 p.
Recommended Citation
Hasanin, Tawfiq, "INVESTIGATING MACHINE LEARNING ALGORITHMS WITH IMBALANCED BIG DATA" (2019). Electronic Theses and Dissertations 2010 - 2019. 2206.
https://digitalcommons.fau.edu/etd_general_6/2206
Identifier
FA00013316
Comments
Includes bibliography.|Dissertation (Ph.D.)--Florida Atlantic University, 2019.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.