Summary of Meta-gcn: a Dynamically Weighted Loss Minimization Method For Dealing with the Data Imbalance in Graph Neural Networks, by Mahdi Mohammadizadeh et al.
Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks
by Mahdi Mohammadizadeh, Arash Mozhdehi, Yani Ioannou, Xin Wang
First submitted to arxiv on: 24 Jun 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 a meta-learning algorithm called Meta-GCN to address class imbalance issues in graph-based classification. Current methods often ignore skewness in class distributions, leading to bias towards majority classes. Instead, Meta-GCN adaptively learns example weights by minimizing unbiased meta-data set loss and optimizing model weights using a small unbiased meta-data set. The proposed method outperforms state-of-the-art frameworks and baselines on two datasets, achieving higher accuracy, AUC-ROC curve, and macro F1-Score. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in many real-world applications like predicting diseases or detecting faults. When classes are not equally distributed, most methods ignore this imbalance and end up favoring the majority class. The new algorithm, Meta-GCN, is designed to fix this issue by adjusting how much each sample counts when making predictions. It works well on two different datasets and outperforms other ways of doing things. |
Keywords
» Artificial intelligence » Auc » Classification » F1 score » Gcn » Meta learning » Roc curve
