FEMa-FS: Finite Element Machines for Feature Selection
Abstract
Identifying anomalies has become one of the primary strategies towards security and protection procedures in computer networks. In this context, machine learning-based methods emerge as an elegant solution to identify such scenarios and learn irrelevant information so that a reduction in the identification time and possible gain in accuracy can be obtained. This paper proposes a novel feature selection approach called Finite Element Machines for Feature Selection (FEMa-FS), which uses the framework of finite elements to identify the most relevant information from a given dataset. Although FEMa-FS can be applied to any application domain, it has been evaluated in the context of anomaly detection in computer networks. The outcomes over two datasets showed promising results.
Cite
@article{arxiv.2212.02507,
title = {FEMa-FS: Finite Element Machines for Feature Selection},
author = {Lucas Biaggi and João P. Papa and Kelton A. P Costa and Danillo R. Pereira and Leandro A. Passos},
journal= {arXiv preprint arXiv:2212.02507},
year = {2022}
}