Automated Feature Selection Algorithm Design as Sequential Decision Making

Automated feature selection algorithm design aims to generate algorithms tailored to individual datasets. However, how to construct and iteratively refine executable feature selection algorithms using dataset analysis and evaluation feedback remains underexplored. Fixed algorithm pools restrict the available design decisions, while difficulties in obtaining reliable evaluation feedback hinder iterative refinement. To address this problem, we propose FSDesign, which decouples algorithm design into an environment that supports design and an LLM-based designer agent that makes design decisions. Their interaction constitutes the algorithm design process, which we formulate as sequential decision making. The environment provides an action space in which algorithms are constructed by composing atomic operators, enabling adaptation beyond a predefined pool of complete algorithms. It also evaluates candidates by jointly considering result profiles and downstream predictive metrics, providing comprehensive feedback for iterative refinement. Based on this feedback and the accumulated interaction history, the designer agent repeatedly constructs and refines candidate algorithms. We conduct a series of experiments, demonstrating the effectiveness of FSDesign.