Document Classification Pattern Recognition via Information Fusion: A Systematic Review of Multimodal and Multiview Representation Approaches
arXiv:2605.23910v1 Announce Type: new Abstract: Information fusion is used widely to improve document classification by the integration of multiple data sources (multimodal) or representations (multiview). However, the field lacks a unified framework, a quantitative synthesis of its effectiveness, and clear guidance for practitioners. This systematic review addresses these gaps by analysing 139 primary studies. It introduces a formal framework to structure the field, presents the results of a qualitative analysis to identify key trends, and performs a random-effects meta-analysis (to our knowl
The proliferation of various AI applications and the increasing complexity of data demand more sophisticated document classification methods, making systematic reviews of information fusion timely.
This review provides a structured overview and quantitative synthesis of multimodal and multiview approaches in document classification, offering a foundational reference for AI practitioners and researchers.
The systematic review offers clearer guidance and a unified framework for integrating diverse data sources in document classification, potentially accelerating advancements in AI's ability to process complex information.
- · AI researchers
- · Data scientists
- · Companies with large document datasets
- · Information retrieval systems
- · Monoview document classification systems
- · Organizations relying on outdated classification methods
Improved accuracy and robustness in document classification across various AI applications.
Faster development and deployment of AI systems that can interpret and categorize diverse data types more effectively.
Enhanced automation of knowledge management and analytical tasks in industries heavily reliant on document processing.
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