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Probabilistic Diagnostic Model for Handling Classifier Degradation in Machine Learning

posted on 04.12.2019 by Gustavo A. Valencia-Zapata
Several studies point out different causes of performance degradation in supervised machine learning. Problems such as class imbalance, overlapping, small-disjuncts, noisy labels, and sparseness limit accuracy in classification algorithms. Even though a number of approaches either in the form of a methodology or an algorithm try to minimize performance degradation, they have been isolated efforts with limited scope. This research consists of three main parts: In the first part, a novel probabilistic diagnostic model based on identifying signs and symptoms of each problem is presented. Secondly, the behavior and performance of several supervised algorithms are studied when training sets have such problems. Therefore, prediction of success for treatments can be estimated across classifiers. Finally, a probabilistic sampling technique based on training set diagnosis for avoiding classifier degradation is proposed


Degree Type

Doctor of Philosophy


Electrical and Computer Engineering

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

Gerhard Klimeck

Additional Committee Member 2

Kadri O. Ersoy

Additional Committee Member 3

Vinayak A. Rao

Additional Committee Member 4

Michael G. Zentner