Summary of Boarding For Iss: Imbalanced Self-supervised: Discovery Of a Scaled Autoencoder For Mixed Tabular Datasets, by Samuel Stocksieker et al.
Boarding for ISS: Imbalanced Self-Supervised: Discovery of a Scaled Autoencoder for Mixed Tabular Datasets
by Samuel Stocksieker, Denys Pommeret, Arthur Charpentier
First submitted to arxiv on: 23 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 A novel approach to self-supervised learning for tabular data is proposed, addressing the specific challenges posed by data imbalance. The paper focuses on autoencoders, widely used for dimensionality reduction and generative model learning. However, existing methods using one-hot encoding with MSE or Cross Entropy loss functions have limitations when dealing with imbalanced categorical variables. To mitigate this, a Multi-Supervised Balanced MSE metric is introduced, reducing reconstruction error by balancing variable influence. Empirical results show that this new metric outperforms the standard MSE in imbalanced datasets and provides similar results in balanced cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps fix a gap in research on self-supervised learning for tabular data. Normally, experts focus on image datasets, but tabular data has different challenges. The main idea is to make autoencoders work better when dealing with mixed data that includes categorical variables. These variables can be tricky because they might not have an equal number of instances (imbalanced). To solve this problem, a new way to calculate the error is proposed: Multi-Supervised Balanced MSE. This helps reduce mistakes and makes learning more accurate. |
Keywords
* Artificial intelligence * Cross entropy * Dimensionality reduction * Generative model * Mse * One hot * Self supervised * Supervised
