Summary of Tabular Learning: Encoding For Entity and Context Embeddings, by Fredy Reusser
Tabular Learning: Encoding for Entity and Context Embeddings
by Fredy Reusser
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
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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 The researchers investigate the impact of various encoding techniques on tabular learning by challenging the commonly used Ordinal encoding method. They apply different preprocessing methods and network architectures across several datasets, resulting in a benchmark that highlights how encoders influence learning outcomes. The study reveals that Ordinal encoding is not the best choice for categorical data preprocessing and classification. Instead, encoding features based on string similarities, computed as a similarity matrix input for the network, yields better results for both entity and context embeddings, particularly for multi-label classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores different ways to prepare data for machine learning models. The researchers tried out various methods to see how they affect the model’s performance. They found that using Ordinal encoding isn’t always the best choice, especially when dealing with categorical data. A better approach is to look at similarities between strings and use those as input for the model. This technique worked well for both entity and context embeddings, especially in situations where multiple labels need to be classified. |
Keywords
* Artificial intelligence * Classification * Machine learning
