RFCBF: enhance the performance and stability of Fast Correlation-Based Filter

TL;DR

This paper introduces RFCBF, an improved feature selection method that enhances the performance and stability of the Fast Correlation-Based Filter by incorporating resampling, leading to better accuracy and runtime in classification tasks.

Contribution

The paper presents RFCBF, a novel extension of FCBF that integrates resampling to improve classification accuracy and stability, outperforming existing methods.

Findings

RFCBF achieves higher classification accuracy.

RFCBF reduces runtime compared to previous methods.

RFCBF demonstrates improved stability across datasets.

Abstract

Feature selection is a preprocessing step which plays a crucial role in the domain of machine learning and data mining. Feature selection methods have been shown to be effctive in removing redundant and irrelevant features, improving the learning algorithm's prediction performance. Among the various methods of feature selection based on redundancy, the fast correlation-based filter (FCBF) is one of the most effective. In this paper, we proposed a novel extension of FCBF, called RFCBF, which combines resampling technique to improve classification accuracy. We performed comprehensive experiments to compare the RFCBF with other state-of-the-art feature selection methods using the KNN classifier on 12 publicly available data sets. The experimental results show that the RFCBF algorithm yields significantly better results than previous state-of-the-art methods in terms of classification accuracy and runtime.