This paper presents a comprehensive evaluation of four ensemble methods (Majority Voting Method, AdaBoost Algorithm, Stacking Ensemble Method, and Bayesian Decision Method) in multi-expert feature selection systems, constructed from five base experts including SelectKBest, Recursive Feature Elimination, Random Forest, L1-based Selection (Lasso), and Mutual Information, in the context of intrusion detection for Internet of Things (IoT) systems. We utilize the CIC IoT 2023 dataset, a large-scale real-world dataset that reflects diverse attack scenarios in IoT environments. The highlight of this research is the integration of Explainable Artificial Intelligence (XAI) methods to analyze the influence of each expert and feature on the ensemble method results. Additionally, the study conducts multi-criteria comparisons including performance metrics (accuracy, per-class precision, recall, F1-score), computational efficiency (training time, response time, memory usage), and comprehensive classification performance indicators (ROC-AUC, PR-AUC, false positive rate). The experimental results provide in-depth analyses of the advantages and limitations of each method, thereby offering recommendations for selecting the most appropriate ensemble method according to specific IoT system requirements. This research contributes to enhancing the effectiveness and transparency of intrusion detection systems in complex IoT environments.