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Information fusion boosts document classification accuracy, review finds

A systematic review of 139 studies on information fusion for document classification reveals that integrating multiple data sources or representations significantly boosts accuracy. Multimodal fusion, in particular, shows a mean accuracy gain of 5.28 percentage points. However, the review highlights a critical lack of reproducibility, with a small percentage of studies employing statistical tests to validate their findings, undermining the reliability of many results. AI

影响 Provides a quantitative synthesis of information fusion techniques for document classification, highlighting accuracy gains and reproducibility challenges.

排序理由 The cluster contains a systematic review paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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  1. arXiv cs.AI TIER_1 English(EN) · Marcin Micha{\l} Miro\'nczuk ·

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

    arXiv:2605.23910v1 Announce Type: cross 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 o…