Summary of Aemlo: Autoencoder-guided Multi-label Oversampling, by Ao Zhou et al.
AEMLO: AutoEncoder-Guided Multi-Label Oversampling
by Ao Zhou, Bin Liu, Jin Wang, Kaiwei Sun, Kelin Liu
First submitted to arxiv on: 23 Aug 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 Class imbalance affects the performance of multi-label classifiers, and oversampling is a popular approach to balance the class distribution. Existing oversampling methods generate synthetic samples through replication or linear interpolation and assign labels based on neighborhood information. However, linear interpolation may not produce diverse enough samples, leading to overfitting issues. Deep learning-based approaches, such as AutoEncoders, have been proposed for generating more diverse and complex synthetic samples, achieving excellent performance on imbalanced binary or multi-class datasets. This study introduces AEMLO, an AutoEncoder-guided Oversampling technique specifically designed for tackling imbalanced multi-label data. AEMLO consists of an encoder-decoder architecture that encodes input data into a low-dimensional feature space, learns its latent representations, and reconstructs it back to its original dimension. The second component is an objective function tailored to optimize the sampling task for multi-label scenarios. Extensive empirical studies show that AEMLO outperforms existing state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how class imbalance affects machine learning models, especially those that predict multiple labels at once. One way to fix this problem is by creating new examples of data that are similar but not exactly the same as the original data. This helps the model learn from more diverse training data and make better predictions. The authors propose a new method called AEMLO that does just this, using a type of deep learning called AutoEncoders. They show that their method performs better than other approaches in tests. |
Keywords
» Artificial intelligence » Autoencoder » Deep learning » Encoder decoder » Machine learning » Objective function » Overfitting
