3MuViS, 3-Phase Multi-View Stacking: A Model Selection Algorithm for Multi-View Fusion

Multi-view learning has emerged as an effective paradigm for integrating heterogeneous data representations in complex classification problems, yet selecting suitable learners for Multi-View Stacking architectures remains computationally challenging and highly dependent on expert decisions. This work proposes 3MuViS, a three-phase methodology for automatic learner selection in Multi-View Stacking that optimizes both view-level learners and the meta-learner according to a target classification metric. The method evaluates candidate machine learning algorithms through cross-validation and constructs optimized stacking configurations. Experiments were conducted on six heterogeneous datasets spanning network intrusion detection, human activity recognition, handwritten digit classification, and transportation mode detection. Performance was evaluated over 25 iterations using the Matthews Correlation Coefficient (MCC), and statistical significance was assessed using Wilcoxon tests. The results show that 3MuViS consistently outperformed stochastic selection strategies and achieved superior or comparable performance to fixed models in five of the six evaluated datasets while frequently approaching the Brute Force upper-bound baseline at a substantially lower computational cost. The findings indicate that jointly optimizing view-level and meta-level learners improves both predictive performance and stability, demonstrating the potential of 3MuViS as a general and efficient framework for multi-view classification problems across diverse domains.