# Fuzzy Rough Choquet Distances for Classification

arXiv: https://arxiv.org/abs/2403.11843 · HTML: https://mlsanity.com/paper/2403.11843/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [Choquet-Based Fuzzy Rough Sets](https://mlsanity.com/paper/2202.10872/) (2022)
- [Fuzzy Rough Sets Based on Fuzzy Quantification](https://mlsanity.com/paper/2212.04327/) (2022)
- [On the Granular Representation of Fuzzy Quantifier-Based Fuzzy Rough Sets](https://mlsanity.com/paper/2312.16704/) (2023)

## Abstract (arXiv)

This paper introduces a novel Choquet distance using fuzzy rough set based measures. The proposed distance measure combines the attribute information received from fuzzy rough set theory with the flexibility of the Choquet integral. This approach is designed to adeptly capture non-linear relationships within the data, acknowledging the interplay of the conditional attributes towards the decision attribute and resulting in a more flexible and accurate distance. We explore its application in the context of machine learning, with a specific emphasis on distance-based classification approaches (e.g. k-nearest neighbours). The paper examines two fuzzy rough set based measures that are based on the positive region. Moreover, we explore two procedures for monotonizing the measures derived from fuzzy rough set theory, making them suitable for use with the Choquet integral, and investigate their differences.