PROBABILISTIC DIAGNOSTIC MODEL FOR HANDLING CLASSIFIER DEGRADATION IN MACHINE LEARNING.pdf (2.89 MB)
Download fileProbabilistic Diagnostic Model for Handling Classifier Degradation in Machine Learning
thesis
posted on 2019-12-04, 20:50 authored by Gustavo A. Valencia-ZapataSeveral 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
History
Degree Type
- Doctor of Philosophy
Department
- Electrical and Computer Engineering
Campus location
- West Lafayette