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

TL;DR

This paper introduces a robust rough set framework that enhances feature selection, classification, and knowledge representation, achieving higher accuracy and efficiency by addressing overfitting and noise issues in data mining.

Contribution

The paper proposes a new robust measurement called relative importance and introduces the concept of rough concept tree for improved knowledge representation and classification.

Findings

Achieves higher accuracy than seven state-of-the-art methods

Addresses overfitting and noise in rough set processing

Demonstrates effectiveness on public benchmark datasets

Abstract

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.