# Confusion matrices and rough set data analysis

## TL;DR

This paper explores the use of confusion matrices within the rough set data model to evaluate classifiers without relying on distributional assumptions, introducing new indices and classifiers based on rough confusion matrices.

## Contribution

It introduces a novel approach combining confusion matrices with rough set theory to assess classifier quality without distributional assumptions.

## Key findings

- Defined indices based on rough confusion matrices
- Developed classifiers using rough set data analysis
- Provided a framework for classifier evaluation without distribution assumptions

## 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.