Using the artificial bee colony technique to optimize machine learning algorithms in estimating the mature weight of camels.
Body weight prediction
Camel
Machine learning
Morphological traits
Optimization
Journal
Tropical animal health and production
ISSN: 1573-7438
Titre abrégé: Trop Anim Health Prod
Pays: United States
ID NLM: 1277355
Informations de publication
Date de publication:
17 Feb 2023
17 Feb 2023
Historique:
received:
19
01
2022
accepted:
11
02
2023
entrez:
17
2
2023
pubmed:
18
2
2023
medline:
22
2
2023
Statut:
epublish
Résumé
This paper aims to predict male and female camels' mature weight (MW) through various morphological traits using hybrid machine learning (ML) algorithms. For this aim, biometrical measurements such as birth weight (BW), length of face (FL), length of the neck (NL), a girth of the heart (HG), body length (BL), withers height (WH), and hind leg length (HLL) were used to estimate the mature weight for eight camel breeds of Pakistan. In this study, multivariate adaptive regression splines (MARS), random forest (RF), and support vector machine (SVM) were applied to develop prediction models. Furthermore, the artificial bee colony (ABC) algorithm is employed to optimize ML models' internal parameters and improve prediction accuracy. The predictive performance of ML and hybrid models was evaluated on a testing dataset using goodness-of-fit measures such as mean absolute deviation (MAD), mean absolute percentage error (MAPE), coefficient of determination (R
Identifiants
pubmed: 36800125
doi: 10.1007/s11250-023-03501-x
pii: 10.1007/s11250-023-03501-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
86Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Nature B.V.
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