# FEMa-FS: Finite Element Machines for Feature Selection

arXiv: https://arxiv.org/abs/2212.02507 · HTML: https://mlsanity.com/paper/2212.02507/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 26 references that are in the map.
- First posted on arXiv 5 Dec 2022.

## Builds on (most cited first, 1 of 1)

- [Flower Pollination Algorithm for Global Optimization](https://mlsanity.com/paper/1312.5673/) (2013)

## Abstract (arXiv)

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.