Award Date
May 2024
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Mathematical Sciences
First Committee Member
Hokwon Cho
Second Committee Member
Malwane Ananda
Third Committee Member
Kaushik Ghosh
Fourth Committee Member
Amei Amei
Fifth Committee Member
Jaewon Lim
Number of Pages
124
Abstract
The subject of Statistical Classification is concerned with identifying and allocating future observations into one of the pre-categorized classes based on the characteristics of the objects. Typically, these decisions to classify and categorize the objects have been dependent on identifying a system of classification, and from there, determining attributes for sorting.
In past decades, from discriminant analysis, various methods have been developed for classification. In particular, the rise of artificial intelligence (AI), machine learning, and statistical learning theory has made it possible to consider improving the existing methods along with new developments and more comprehensive schemes in conjunction with data-driven methods.
In this dissertation, we propose innovation using multiple decision-theoretic perspectives, such as the Indifference-Zone (IZ) approach and the Subset Selection (SS) method, to improve and clarify how these classification decisions can be made.
Keywords
Indifference-Zone Approach; Multiple Decision Problem; Preferable Predictor Vector; Selection and Ranking Methodologies; Statistical Classification; Statistical Learning
Disciplines
Statistics and Probability
Degree Grantor
University of Nevada, Las Vegas
Language
English
Repository Citation
Lee, Jeong Jun, "Statistical Classification Using Selection and Ranking Methodologies with Statistical Learning" (2024). UNLV Theses, Dissertations, Professional Papers, and Capstones. 5025.
http://dx.doi.org/10.34917/37650849
Rights
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