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Various considerations on performanc...

Nyongesa, Denis Barasa.

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Various considerations on performance measures for a classification of ordinal data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Various considerations on performance measures for a classification of ordinal data./
作者:	Nyongesa, Denis Barasa.
面頁冊數:	111 p.
附註:	Source: Masters Abstracts International, Volume: 55-05.
Contained By:	Masters Abstracts International55-05(E).
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10133995
ISBN:	9781339924977

Various considerations on performance measures for a classification of ordinal data.
Nyongesa, Denis Barasa.

Various considerations on performance measures for a classification of ordinal data. - 111 p.

Source: Masters Abstracts International, Volume: 55-05.

Thesis (M.S.)--California State University, Long Beach, 2016.

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.

ISBN: 9781339924977Subjects--Topical Terms:

517247
Statistics.

Various considerations on performance measures for a classification of ordinal data.
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