Harnessing Multimodal Data Fusion and Adaptive Learning Pipelines for Scalable Predictive Modeling and Decision Support in High-Dimensional and Heterogeneous Data Science Environments
Keywords:
Multimodal data fusion, adaptive pipelines, high-dimensional data, decision support systems, ensemble learning, heterogeneous data modeling, scalable AIAbstract
The exponential growth of high-dimensional, heterogeneous data across domains such as healthcare, finance, climate science, and smart cities has necessitated advanced predictive modeling frameworks that can integrate multimodal inputs and adaptively learn from dynamic environments. In this paper, we present a conceptual architecture that combines multimodal data fusion with adaptive machine learning pipelines for scalable decision support. We explore the relevance of combining structured and unstructured data sources using late and early fusion strategies, incorporating feature selection and ensemble modeling for robustness. Further, we address challenges of data imbalance, domain drift, and computational scalability. We argue that this hybrid approach not only enhances model interpretability and generalizability but also provides practical value for real-time analytics and decision-making. This paper reviews pre-2024 literature, proposes a scalable fusion-learning framework, and illustrates it with synthetic simulations using benchmark datasets.
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