Summary of Neuro-symbolic Embedding For Short and Effective Feature Selection Via Autoregressive Generation, by Nanxu Gong et al.
Neuro-Symbolic Embedding for Short and Effective Feature Selection via Autoregressive Generation
by Nanxu Gong, Wangyang Ying, Dongjie Wang, Yanjie Fu
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper proposes a novel generative framework for feature selection that reformulates the problem through a neuro-symbolic lens. The goal is to identify short and effective feature subsets that can improve model performance while reducing redundancy. The approach involves collecting feature selection samples, encoding them into a continuous embedding space using an encoder-decoder-evaluator learning paradigm, and then searching for robust and generalized embeddings using a multi-gradient search algorithm. The proposed framework is evaluated through comprehensive experiments and case studies, showing its effectiveness in improving model performance and reducing feature subset redundancy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how to pick the right features from a big group of features that are given. This is important because having too many features can make it harder for computers to learn from them. Right now, most methods use just one way to decide which features are best, but this might not be good enough because it might not work well in different situations. The new approach uses a combination of artificial intelligence and traditional computer science techniques to find the best features that will work well in many situations. It does this by collecting lots of information about the features, using that information to create a special kind of map, and then searching for the best way to use those features. |
Keywords
» Artificial intelligence » Embedding space » Encoder decoder » Feature selection
