Research on an Improved Multi-Model Dynamic Fusion Classification Technique

Existing multi-model fusion methods generally adopt a global, fixed fusion strategy, applying all base models and weighting rules uniformly to all samples to be classified and all categories, thereby lacking adaptability to specific samples and category-specific targeting. Most traditional dynamic selection and dynamic weighting fusion methods only implement global parameter adjustments at the sample level, without considering the significant differences in category-specific capabilities among the base models. When a single base model exhibits superior recognition capabilities for only certain categories, its prediction accuracy and confidence for the remaining categories are low. If all base models are fused directly, poor-quality class predictions can cause negative interference and even dominate the final decision, leading to classification errors. Furthermore, traditional dynamic fusion suffers from computational redundancy, difficulty in suppressing interference from low-confidence samples, and the challenge of balancing dynamic optimization with inference efficiency. To address these issues, this paper proposes an improved multi-model dynamic fusion classification technique that differs from the traditional global dynamic fusion paradigm. By constructing a voting matrix, contribution weights, and a matrix of effective category voting weights, this method establishes a category-level model performance evaluation and differentiated weighting mechanism. This enables the precise selection of superior base models for each sample and category, thereby filtering out interference from low-confidence and suboptimal category predictions. At the same time, in the network architecture design, lightweight models are organized into a branch structure, and effective branches are dynamically activated as needed to participate in decision-making, significantly reducing the computational overhead of inference. To validate the fusion classification technique proposed in this paper, for the experiments, we selected mainstream lightweight models such as MobileNetV2, EfficientNetB0, ShuffleNetv2, MNASNet 0.75, and MobileNetV3_Small for evaluation on the NEU dataset and NASA’s Milling Data Set. The experimental results demonstrate that the fusion method proposed in this paper can fully aggregate the category-specific strengths of different lightweight models, effectively mitigate the risk of misclassification associated with traditional fusion methods, and enhance model robustness while ensuring high classification accuracy. It achieves classification performance comparable to that of large deep models with extremely low computational overhead. This method is not only suitable for application in multiple-criteria decision-making but can also be implemented and extended to multi-source/multi-modal data fusion and deep neural networks, making it of practical value.