Summary of Hypersmote: a Hypergraph-based Oversampling Approach For Imbalanced Node Classifications, by Ziming Zhao et al.
HyperSMOTE: A Hypergraph-based Oversampling Approach for Imbalanced Node Classifications
by Ziming Zhao, Tiehua Zhang, Zijian Yi, Zhishu Shen
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes HyperSMOTE, a novel method to alleviate class imbalance issues in hypergraph learning. Unlike traditional graphs, hypergraphs better model higher-order relationships among nodes. Existing techniques like GraphSMOTE improve minority sample classification accuracy but struggle with hypergraph structure. Inspired by SMOTE, HyperSMOTE synthesizes new minority class nodes and integrates them into the original hypergraph using a decoder trained on the incidence matrix. The method is evaluated on single-modality datasets (Cora, Cora-CA, Citeseer) and multimodal conversations (MELD), showing an average performance gain of 3.38% and 2.97% in accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand relationships between things by using something called hypergraphs. Hypergraphs are like super-powerful connections that help us see how different things relate to each other. The problem is, when we have lots of data, some types of information might be more important or common than others. This can make the computer biased towards those common things and miss out on important details. To fix this, the authors came up with a new way called HyperSMOTE that helps balance the data so the computer can see all the relationships correctly. |
Keywords
» Artificial intelligence » Classification » Decoder
