Hierarchical MixUp Multi-label Classification with Imbalanced Interdisciplinary Research Proposals

• URL: http://arxiv.org/abs/2209.13912v2
• Date: Wed, 28 Jun 2023 14:24:54 GMT
• Title: Hierarchical MixUp Multi-label Classification with Imbalanced Interdisciplinary Research Proposals
• Authors: Meng Xiao, Min Wu, Ziyue Qiao, Zhiyuan Ning, Yi Du, Yanjie Fu, Yuanchun Zhou
• Abstract summary: We propose a hierarchical mixup multiple-label classification framework, which we called H-MixUp. The number of proposals is imbalanced between non-interdisciplinary and interdisciplinary research. We develop a fused training method of Wold-level MixUp, Word-level CutMix, Manifold MixUp, and Document-level MixUp to address the third issue.
• Score: 22.458438099629277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Funding agencies are largely relied on a topic matching between domain experts and research proposals to assign proposal reviewers. As proposals are increasingly interdisciplinary, it is challenging to profile the interdisciplinary nature of a proposal, and, thereafter, find expert reviewers with an appropriate set of expertise. An essential step in solving this challenge is to accurately model and classify the interdisciplinary labels of a proposal. Existing methodological and application-related literature, such as textual classification and proposal classification, are insufficient in jointly addressing the three key unique issues introduced by interdisciplinary proposal data: 1) the hierarchical structure of discipline labels of a proposal from coarse-grain to fine-grain, e.g., from information science to AI to fundamentals of AI. 2) the heterogeneous semantics of various main textual parts that play different roles in a proposal; 3) the number of proposals is imbalanced between non-interdisciplinary and interdisciplinary research. Can we simultaneously address the three issues in understanding the proposal's interdisciplinary nature? In response to this question, we propose a hierarchical mixup multiple-label classification framework, which we called H-MixUp. H-MixUp leverages a transformer-based semantic information extractor and a GCN-based interdisciplinary knowledge extractor for the first and second issues. H-MixUp develops a fused training method of Wold-level MixUp, Word-level CutMix, Manifold MixUp, and Document-level MixUp to address the third issue.

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