Summary of Semantic-guided Rl For Interpretable Feature Engineering, by Mohamed Bouadi et al.
Semantic-Guided RL for Interpretable Feature Engineering
by Mohamed Bouadi, Arta Alavi, Salima Benbernou, Mourad Ouziri
First submitted to arxiv on: 3 Oct 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 A machine learning paper introduces a hybrid approach called SMART, which uses semantic technologies and deep reinforcement learning to generate interpretable features. The approach, referred to as Automated Feature Engineering (AutoFE), aims to improve predictive accuracy by exploiting domain-specific knowledge embedded in Knowledge Graphs (KG). Two steps are employed: Exploitation and Exploration. Exploitation uses Description Logics (DL) to reason on semantics and infer features, while Exploration uses DRL to guide the search space. The paper demonstrates that SMART improves prediction accuracy and ensures interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated Feature Engineering is important for improving machine learning model quality. To do this, we need to generate high-quality features. One way is through a process called Feature Engineering. However, this is time-consuming and requires domain-specific knowledge. A new approach called SMART helps solve this problem by using semantic technologies and deep reinforcement learning. This makes it easier to understand how the features were generated. The results show that SMART works well and can improve prediction accuracy. |
Keywords
» Artificial intelligence » Feature engineering » Machine learning » Reinforcement learning » Semantics
