FEMa-FS: Finite Element Machines for Feature Selection
Lucas Biaggi, João Paulo Papa, Kelton Augusto Pontara da Costa, Danillo Roberto Pereira, Leandro Aparecido Passos Júnior · 2022 26th International Conference on Pattern Recognition (ICPR) · 2022
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.