aspg-18-3579-1792533590 20261020215950 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 16 1 Dipti Theng Department of Computer Science and Engineering, Symbiosis Institute of Technology Pune, Symbiosis International (Deemed University), Pune, India K. K. Bhoyar Department of Computer Science and Engineering YCCE Nagpur, India Prashant Pawade Department of Civil Engineering GHRCE Nagpur, India Selecting the most relevant feature subset for a task is demanded and recommended for high accuracy and reduced model training time. Ensemble learning has shown superior results in classification; hence, we propose an ensemble method for feature selection and shown stability analysis for the selected feature set. The research question being investigated is whether ensemble methods are effective at selecting informative features in a dataset and if the selected features are stable compared to other feature selection methods. This paper presented a tree-based ensemble learning approach for feature selection. Our approach for ensemble feature selection includes function perturbation with the voting ensemble, an ensemble with a fixed number of features, and an ensemble with a contiguous number of features. Ensemble learning is found to be superior to other traditional feature selection algorithms. Ensemble learning algorithms are implemented on two high-dimensional microarray biomedical datasets. From our experimental study, it is observed that the voting ensemble outperforms other ensemble techniques, thereby reducing feature subset size and achieving higher accuracy. Stability analysis of all the algorithms has been studied and it is found that all ensemble techniques have higher stability than the traditional feature selection methods. Thus, ensemble learning proves to be a superior technique for feature selection. Our results demonstrate that the proposed method is effective in identifying relevant features and stable features and can improve the performance of machine learning models. 2025 41 48 3579 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.160104 https://www.americaspg.com/journal/18/article/3579 https://www.americaspg.com/storage/41746288547.pdf