Data Augmentation and Machine Learning algorithms for multi-class imbalanced morphometrics data of stingless bees

Data Augmentation and Machine Learning algorithms for multi-class imbalanced morphometrics data of stingless bees

The study focusses on handling of multiclass imbalanced data on classification of stingless bee samples by employing data balancing techniques, namely Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) approach. These techniques are applied in combination with machine...

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Bibliographic Details
Main Authors: Daisy Salifu, Lorna Chepkemoi, Eric Ali Ibrahim, Kiatoko Nkoba, Henri E.Z. Tonnang
Format: Article
Language:English
Published: Elsevier 2025-02-01
Series:Heliyon
Subjects:
Imbalanced data
Stingless bees
Random forest
SVM
SMOTE
ADASYN
Online Access:http://www.sciencedirect.com/science/article/pii/S2405844025005948
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