A concise method for feature selection via normalized frequencies

TL;DR

This paper introduces a simple, unified feature selection method combining filter and wrapper techniques using normalized frequencies and mutual information, leading to improved performance in intrusion detection datasets.

Contribution

A novel feature selection approach that simplifies the process by fusing filter and wrapper methods with normalized frequencies and mutual information, avoiding complex metaheuristics.

Findings

Outperformed state-of-the-art methods in accuracy, precision, recall, F-score, and AUC

Effective feature subset selection for intrusion detection datasets

Demonstrated efficiency and improved model performance

Abstract

Feature selection is an important part of building a machine learning model. By eliminating redundant or misleading features from data, the machine learning model can achieve better performance while reducing the demand on com-puting resources. Metaheuristic algorithms are mostly used to implement feature selection such as swarm intelligence algorithms and evolutionary algorithms. However, they suffer from the disadvantage of relative complexity and slowness. In this paper, a concise method is proposed for universal feature selection. The proposed method uses a fusion of the filter method and the wrapper method, rather than a combination of them. In the method, one-hoting encoding is used to preprocess the dataset, and random forest is utilized as the classifier. The proposed method uses normalized frequencies to assign a value to each feature, which will be used to find the optimal feature subset. Furthermore, we propose a novel approach to exploit the outputs of mutual information, which allows for a better starting point for the experiments. Two real-world dataset in the field of intrusion detection were used to evaluate the proposed method. The evaluation results show that the proposed method outperformed several state-of-the-art related works in terms of accuracy, precision, recall, F-score and AUC.