# Decision Level Fusion: An Event Driven Approach

arXiv: https://arxiv.org/abs/1809.09166 · HTML: https://mlsanity.com/paper/1809.09166/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2018.

## Built on by (most cited first, 2 of 2)

- [Latent Code-Based Fusion: A Volterra Neural Network Approach](https://mlsanity.com/paper/2104.04829/) (2021)
- [Information Fusion: Scaling Subspace-Driven Approaches](https://mlsanity.com/paper/2204.12035/) (2022)

## Abstract (arXiv)

This paper presents a technique that combines the occurrence of certain events, as observed by different sensors, in order to detect and classify objects. This technique explores the extent of dependence between features being observed by the sensors, and generates more informed probability distributions over the events. Provided some additional information about the features of the object, this fusion technique can outperform other existing decision level fusion approaches that may not take into account the relationship between different features.