<ul><li>Effective feature selection, representation, and transformation play a crucial role in enhancing machine learning models.</li><li>Reinforcement learning (RL) provides a new perspective for optimal feature subset exploration using multi-agent and single-agent models.</li><li>Interactive reinforcement learning integrated with decision tree and diversified teaching strategies improve selection efficiency and quality.</li><li>The use of Monte Carlo-based reinforced feature selection and a dual-agent RL framework further enhances computational efficiency and captures interactions between features and instances.</li></ul>

Automation and Feature Selection Enhancement with Reinforcement Learning (RL)

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