# An Efficient and Accurate Rough Set for Feature Selection, Classification and Knowledge Representation

arXiv: https://arxiv.org/abs/2201.00436 · HTML: https://mlsanity.com/paper/2201.00436/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 56 references that are in the map.
- First posted on arXiv 29 Dec 2021.

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

- [Infinite Feature Selection: A Graph-based Feature Filtering Approach](https://mlsanity.com/paper/2006.08184/) (2020)
- [Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach](https://mlsanity.com/paper/1707.07538/) (2017)
- [Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality](https://mlsanity.com/paper/1704.05409/) (2017)

## Built on by (most cited first, 5 of 5)

- [Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method](https://mlsanity.com/paper/2304.11171/) (2023)
- [A Unified Granular-ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set](https://mlsanity.com/paper/2201.03349/) (2022)
- [Open Continual Feature Selection via Granular-Ball Knowledge Transfer](https://mlsanity.com/paper/2403.10253/) (2024)
- [Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls](https://mlsanity.com/paper/2501.02975/) (2025)
- [A robust three-way classifier with shadowed granular-balls based on justifiable granularity](https://mlsanity.com/paper/2407.11027/) (2024)

## Abstract (arXiv)

This paper present a strong data mining method based on rough set, which can realize feature selection, classification and knowledge representation at the same time. Rough set has good interpretability, and is a popular method for feature selections. But low efficiency and low accuracy are its main drawbacks that limits its application ability. In this paper,corresponding to the accuracy, we first find the ineffectiveness of rough set because of overfitting, especially in processing noise attribute, and propose a robust measurement for an attribute, called relative importance.we proposed the concept of "rough concept tree" for knowledge representation and classification. Experimental results on public benchmark data sets show that the proposed framework achieves higher accurcy than seven popular or the state-of-the-art feature selection methods.