Dissertation/Thesis Abstract

Various considerations on performance measures for a classification of ordinal data
by Nyongesa, Denis Barasa, M.S., California State University, Long Beach, 2016, 111; 10133995
Abstract (Summary)

The technological advancement and the escalating interest in personalized medicine has resulted in increased ordinal classification problems. The most commonly used performance metrics for evaluating the effectiveness of a multi-class ordinal classifier include; predictive accuracy, Kendall's tau-b rank correlation, and the average mean absolute error (AMAE). These metrics are beneficial in the quest to classify multi-class ordinal data, but no single performance metric incorporates the misclassification cost. Recently, distance, which finds the optimal trade-off between the predictive accuracy and the misclassification cost was proposed as a cost-sensitive performance metric for ordinal data. This thesis proposes the criteria for variable selection and methods that accounts for minimum distance and improved accuracy, thereby providing a platform for a more comprehensive and comparative analysis of multiple ordinal classifiers. The strengths of our methodology are demonstrated through real data analysis of a colon cancer data set.

Indexing (document details)
Advisor: Moon, Hojin
Commitee: Kim-Park, Yong Hee, Suaray, Kagba
School: California State University, Long Beach
Department: Mathematics and Statistics
School Location: United States -- California
Source: MAI 55/05M(E), Masters Abstracts International
Subjects: Biostatistics, Statistics, Artificial intelligence
Keywords: Classification trees, Colon cancer, Logistic regression, Ordinal data, Random forests, Support vector machines
Publication Number: 10133995
ISBN: 978-1-339-92497-7
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