# Confusion matrices and rough set data analysis

arXiv: https://arxiv.org/abs/1902.01487 · HTML: https://mlsanity.com/paper/1902.01487/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2019.

## Built on by (most cited first, 2 of 2)

- [A Model-Mediated Stacked Ensemble Approach for Depression Prediction Among Professionals](https://mlsanity.com/paper/2506.14459/) (2025)
- [Development of a Vision System to Enhance the Reliability of the Pick-and-Place Robot for Autonomous Testing of Camera Module used in Smartphones](https://mlsanity.com/paper/2305.04605/) (2023)

## Abstract (arXiv)

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.