Auto-CASH: Autonomous Classification Algorithm Selection with Deep Q-Network

TL;DR

Auto-CASH leverages deep reinforcement learning to automate the selection of classification algorithms and hyperparameters efficiently, significantly reducing human effort and computational time while improving performance on real-world datasets.

Contribution

It introduces Auto-CASH, the first method using Deep Q-Networks for automatic meta-feature selection in CASH, enhancing efficiency and effectiveness.

Findings

Auto-CASH outperforms classical CASH methods in accuracy.

Auto-CASH reduces time cost significantly.

Experimental results on 120 datasets validate its effectiveness.

Abstract

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.