Auto-CASH: Autonomous Classification Algorithm Selection with Deep Q-Network
It addresses the challenge of automated algorithm selection and hyperparameter optimization for machine learning tasks, reducing time and human effort, though it appears incremental as it builds on existing CASH approaches.
The paper tackles the CASH problem by introducing Auto-CASH, a pre-trained model using Deep Q-Network to automatically select meta-features for datasets, achieving better performance in shorter time on 120 real-world classification datasets.
The great amount of datasets generated by various data sources have posed the challenge to machine learning algorithm selection and hyperparameter configuration. For a specific machine learning task, it usually takes domain experts plenty of time to select an appropriate algorithm and configure its hyperparameters. If the problem of algorithm selection and hyperparameter optimization can be solved automatically, the task will be executed more efficiently with performance guarantee. Such problem is also known as the CASH problem. Early work either requires a large amount of human labor, or suffers from high time or space complexity. In our work, we present Auto-CASH, a pre-trained model based on meta-learning, to solve the CASH problem more efficiently. Auto-CASH is the first approach that utilizes Deep Q-Network to automatically select the meta-features for each dataset, thus reducing the time cost tremendously without introducing too much human labor. To demonstrate the effectiveness of our model, we conduct extensive experiments on 120 real-world classification datasets. Compared with classical and the state-of-art CASH approaches, experimental results show that Auto-CASH achieves better performance within shorter time.