Confusion matrices and rough set data analysis

Abstract

A widespread approach in machine learning to evaluate the quality of a classifier is to cross -- classify predicted and actual decision classes in a confusion matrix, also called error matrix. A classification tool which does not assume distributional parameters but only information contained in the data is based on the rough set data model which assumes that knowledge is given only up to a certain granularity. Using this assumption and the technique of confusion matrices, we define various indices and classifiers based on rough confusion matrices.

Equal authorship implied. To appear in the Proceedings of the 2019 International Conference on Pattern Recognition and Intelligent Systems (PRIS 2019)