Meta-model integration with attention mechanisms for advanced decision-level fusion in machine learning

Abstract

This work proposes an advanced meta-model approach that incorporates forecasts from multiple machine learning models to improve classification accuracy in complex tasks. The approach employs decision-level data fusion, where predictions from random forest (RF), XGBoost, neural networks (NN), and support vector machine (SVM) are combined within a meta-model framework. The meta-model incorporates an attention mechanism and a gated model selection process to dynamically emphasize the most relevant model outputs based on input features. The results demonstrate superior accuracy in predicting explicit content compared to traditional fusion methods. This research highlights the potential of attention-enhanced meta-models in improving interpretability and accuracy across various domains. The integration of meta-models with attention mechanisms has the potential to significantly enhance decision-level fusion in machine learning applications. This study investigates the development of an advanced fusion framework leveraging attention mechanisms to improve decision-making accuracy in multi-source data environments. The proposed method is evaluated across multiple datasets, demonstrating its efficacy in increasing predictive performance and robustness.