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Doctoral dissertation · Computational Intelligence · 2006

Assessment of Statistical Classification Rules: Implications for Computational Intelligence

A nonparametric approach to measuring classifier performance when the available data are limited.

Research overview

Assessing classifiers without assuming a particular data distribution.

The dissertation develops a general framework for evaluating binary classification rules through receiver operating characteristic analysis. It considers full and partial area under the ROC curve, performance at a chosen operating point, and the uncertainty introduced when training and assessment must share a limited dataset.

The methods apply across classical and modern classifier architectures, with particular relevance to medical diagnostics and other high-stakes decision systems.

Statistical classificationClassifier assessmentROC analysisNonparametric statisticsComputational intelligenceMedical diagnostics

Suggested citation: Yousef, Waleed Ahmed. “Assessment of Statistical Classification Rules: Implications for Computational Intelligence.” Doctoral dissertation, The George Washington University, 2006.