An Efficient Weighted Majority Voting Ensemble Machine Learning Classifier Framework for Image Segmentation

Zahra Faska; Lahbib Khrissi; Imadeddine Mountasser; Khalid Haddouch; Nabil El Akkad; Walid El-Shafai; Abrar Fallatah; Ahmad Taher Azar; Saim Ahmed

doi:10.48084/etasr.16623

Authors

Zahra Faska Laboratory of Applied Sciences and Emerging Technologies, ENSA, Sidi Mohamed Ben Abdellah University, Fez, Morocco
Lahbib Khrissi Laboratory of Applied Sciences and Emerging Technologies, ENSA, Sidi Mohamed Ben Abdellah University, Fez, Morocco
Imadeddine Mountasser Laboratory of Applied Sciences and Emerging Technologies, ENSA, Sidi Mohamed Ben Abdellah University, Fez, Morocco
Khalid Haddouch Laboratory of Applied Sciences and Emerging Technologies, ENSA, Sidi Mohamed Ben Abdellah University, Fez, Morocco
Nabil El Akkad Laboratory of Applied Sciences and Emerging Technologies, ENSA, Sidi Mohamed Ben Abdellah University, Fez, Morocco
Walid El-Shafai Automated Systems and Computing Lab, Prince Sultan University, Riyadh, Saudi Arabia | Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menoufia, Egypt
Abrar Fallatah College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia | Automated Systems and Computing Lab, Prince Sultan University, Riyadh, Saudi Arabia
Ahmad Taher Azar College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia | Automated Systems and Computing Lab, Prince Sultan University, Riyadh, Saudi Arabia
Saim Ahmed Automated Systems and Computing Lab, Prince Sultan University, Riyadh, Saudi Arabia

Volume: 16 | Issue: 3 | Pages: 35220-35237 | June 2026 | https://doi.org/10.48084/etasr.16623

Received: 30 November 2025 | Revised: 2 February 2026 | Accepted: 13 February 2026 | Online: 15 April 2026

Corresponding author: Abrar Fallatah

Abstract

Ensemble learning is an effective approach to improving the robustness and accuracy of image segmentation by combining multiple classifiers. This study presents an efficient Weighted Majority Voting Ensemble (WMVE) framework integrating five Machine Learning (ML)-based segmentation models: Random Forest (RF), Naïve Bayes (NB), eXtreme Gradient Boosting (XGB), K-Nearest Neighbors (KNN), and Multilayer Perceptron (MLP). The standard preprocessing techniques include Gaussian smoothing and median filtering to ensure reliable feature extraction and high-quality segmentation. The proposed WMVE allows assigning specific weights to each classifier according to its validation accuracy result for adaptive informed decision fusion, where performance evaluation uses region-based segmentation metrics, including Segmentation Covering (SC), Probabilistic Rand Index (PRI), Variation of Information (VoI), Global Consistency Error (GCE), and Boundary Displacement Error (BDE). Experimental results indicate that the proposed ensemble is better than single classifiers and nearly as good as existing ensemble and deep clustering approaches on several datasets. Therefore, the WMVE framework can be considered a strong approach to attain high performance in image segmentation, since experimental results also show near-optimal performance with existing state-of-the-art methods.

Keywords:

image segmentation, ensemble learning, random forest, KNN, Bayesian network, XGB, MLP, voting, weighted majority voting

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