2026-05-26原文

Document Classification Pattern Recognition via Information Fusion: A Systematic Review of Multimodal and Multiview Representation Approaches

This systematic review of 139 studies proposes a unified framework and meta-analysis. Results show multimodal fusion improves accuracy by 5.28% on average, multiview fusion boosts accuracy by 4.67% and F1 by 3.08%, but only a minority of studies used statistical tests, raising reproducibility concerns.

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Key points

Meta-analysis reveals that multimodal and multiview fusion significantly improve document classification accuracy.
Multimodal fusion yields a +5.28% accuracy gain; multiview yields +4.67% accuracy and +3.08% F1 score.
Only 11.8% of multimodal and 23.3% of multiview studies employ statistical tests, undermining reliability.
Effective fusion depends on strategic alignment with task context and rigorous validation, not algorithmic complexity.

Why it matters

This matters because meta-analysis reveals that multimodal and multiview fusion significantly improve document classification accuracy.

Technical impact

May affect model selection, inference cost, product capability, and evaluation benchmarks.

[2605.23910] Document Classification Pattern Recognition via Information Fusion: A Systematic Review of Multimodal and Multiview Representation Approaches

[Submitted on 7 Apr 2026]

Title:Document Classification Pattern Recognition via Information Fusion: A Systematic Review of Multimodal and Multiview Representation Approaches

View a PDF of the paper titled Document Classification Pattern Recognition via Information Fusion: A Systematic Review of Multimodal and Multiview Representation Approaches, by Marcin Micha{\l} Miro\'nczuk

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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 knowledge, the first focused on document classification) to quantify performance gains. Our meta-analysis reveals that multimodal fusion improves accuracy (mean gain of +5.28 percentage points, $p=0.0016$) significantly -- the F1-score effect is directionally positive but statistically non-significant in our primary model. Multiview fusion provides consistent but modest gains for accuracy (+4.67\%), F1-score (+3.08\%), and recall (all $p

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