@inbook{05655ae1bc494b6a925e30aab4fe92c5,
title = "ELM Feature Selection and SOM Data Visualization for Nursing Survey Datasets",
abstract = "This paper presents a novel methodology to analyze nursing surveys. It is based on ELM and SOM. The goal is to identify which variables lead to the likelihood to report the medication errors. ELM are accurate by extremely fast prediction models. SOM are performing nonlinear dimensionality reduction to get an accurate visualization of the data. Combining both techniques reduces the curse of dimensionality and improves furthermore the interpretability of the visualization. The methodology is tested on a nursing survey datasets.",
author = "Renjie Hu and Amany Farag and Kaj-Mikael Bj{\"o}rk and Amaury Lendasse",
year = "2021",
doi = "10.1007/978-3-030-58989-9_11",
language = "English",
isbn = "978-3-030-58988-2",
series = "Proceedings of ELM2019",
publisher = "Springer",
pages = "99--108",
editor = "Jiuwen Cao and Vong, {Chi Man} and Yoan Miche and Amaury Lendasse",
booktitle = "Proceedings of ELM2019",
address = "International",
}