Теорія Демпстера-Шафера для задач класифікації - Лузан, М. О. (ХНУРЕ, 2019)
Today classification tasks are highly used in many different cases from detecting fraud in banks and faults on electric stations to sports betting. Such algorithms as Logistic Regression, Naive Bayes, MLP etc. are in demand. But one of the bottlenecks of these methods is problem when appears uncertainty. In this case classifiers based on Dempster-Shafer theory provides good performance. It converts features into Dempster-Shafer mass function that gives normalized plausibilities which are more informative than common output probability distribution from other classifiers. The theory allows to determine the degree of belief (confidence) in the classifier’s decision.