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Reinforcement Learning-based Feature Generation Algorithm for Scientific Data

Created by
  • Haebom

Author

Meng Xiao, Junfeng Zhou, Yuanchun Zhou

Outline

This paper focuses on feature generation (FG) for improving the prediction performance of scientific data. Existing feature generation methods have the problems of expertise and computational cost for generating high-dimensional feature combinations. In this paper, we propose a multi-agent feature generation (MAFG) framework based on the data-driven artificial intelligence (DCAI) paradigm. MAFG is an iterative exploration process in which multiple agents generate high-dimensional feature combinations through reinforcement learning and evaluate the generated features using a large-scale language model (LLM). Experimental results show that the MAFG framework automates the feature generation process and improves the performance in scientific data analysis tasks.

Takeaways, Limitations

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Takeaways:
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A novel framework for automating the generation of high-dimensional feature combinations in scientific data analysis
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An innovative approach combining multi-agent, reinforcement learning, and LLM
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Demonstrated performance improvements in a variety of scientific data mining tasks
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Limitations:
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Further research is needed on the generalization performance of the MAFG framework and its applicability to various data types.
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Need for analysis of the dependence of LLM on interpretability and the impact of LLM limitations on MAFG performance
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Further research is needed on the interactions and collaboration strategies between agents.
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