Outlier Detection in MET Data Using Subspace Outlier Detection Method

Dupuy Charles, Dupuy Charles, Pascal Pultrini, Andrea Tettamanzi

2024

Abstract

In plant breeding, Multi-Environment Field Trials (MET) are commonly used to evaluate genotypes for multiple traits and to estimate their genetic breeding value using Genomic Prediction (GP). The occurrence of outliers in MET is common and is known to have a negative impact on the accuracy of the GP. Therefore, identification of outliers in MET prior to GP analysis can lead to better results. However, Outlier Detection (OD) in MET is often overlooked. Indeed, MET give rise to different level of residuals which favor the presence of swamping and masking effects where ideal sample points may be portrayed as outliers instead of the true ones. Consequently, without a sensitive and robust outlier detection algorithm, OD can be a waste of time and potentially degrade the accuracy prediction of the GP, especially when the data set is not huge. In this study, we compared various robust outlier methods from different approaches to determine which one is most suitable for identifying MET anomalies. Each method has been tested on eleven real-world MET data sets. Results are validated by injecting a proportion of artificial outliers in each set. The Subspace Outlier Detection Method stands out as the most promising among the tested methods.

Paper Citation

in Harvard Style

Charles D., Pultrini P. and Tettamanzi A. (2024). Outlier Detection in MET Data Using Subspace Outlier Detection Method. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 243-250. DOI: 10.5220/0012318000003636

in Bibtex Style

@conference{icaart24,
author={Dupuy Charles and Pascal Pultrini and Andrea Tettamanzi},
title={Outlier Detection in MET Data Using Subspace Outlier Detection Method},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={243-250},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012318000003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Outlier Detection in MET Data Using Subspace Outlier Detection Method
SN - 978-989-758-680-4
AU - Charles D.
AU - Pultrini P.
AU - Tettamanzi A.
PY - 2024
SP - 243
EP - 250
DO - 10.5220/0012318000003636
PB - SciTePress