FEMa-FS: Finite Element Machines for Feature Selection

Author(s)

Biaggi, Lucas ; Papa, Joao P. ; Costa, Kelton A. P. ; Pereira, Danillo R. ; Passos, Leandro A. ; IEEE

• Document type: Journal article
• Source: 2022 26TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR); v. N/A, p. 8-pg., 2022-01-01.

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. (AU)

• FAPESP's process: 21/05516-1 - On the application of Explainable Artificial Intelligence (XAI) techniques for generating images from data packages to detect anomalies in computer networks
• Grantee: Kelton Augusto Pontara da Costa
• Support Opportunities: Regular Research Grants
• FAPESP's process: 17/22905-6 - About image security using machine learning
• Grantee: Kelton Augusto Pontara da Costa
• Support Opportunities: Regular Research Grants
• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 16/19403-6 - Energy-based learning models and their applications
• Grantee: João Paulo Papa
• Support Opportunities: Regular Research Grants
• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants