Auto-CASH: A meta-learning embedding approach for autonomous classification algorithm selection

With years of development, machine learning algorithms have excellent performance in some tasks of data analysis and data mining. To apply machine learning to new tasks, suitable algorithm and hyperparameters selection techniques, which is known as Combined Algorithm Selection and Hyperparameter optimization problem, are in demand. In the field of data analysis, how to automate the algorithm selection process has become a hot research topic in recent years. Most of the existing approaches are developed under the background of Automated Machine Learning with high time or space complexity. To alleviate the issue, an approach extracts and learns from prior experience based on meta-learning theory named Auto-CASH is proposed in this paper. One of the major drawbacks of existing meta-learning methods is that they rely too much on human expertise to extract and filter knowledge that guides subsequent training. Auto-CASH can automatically select features of tasks by introducing a reinforcement learning strategy. Thus Auto-CASH becomes less dependent on human expertise. Besides, two pruning strategies when processing Hyperparameter Optimization to improve efficiency are firstly proposed. Extensive experiments on classification tasks are conducted and results demonstrate that Auto-CASH outperforms state-of-the-art CASH approaches and popular AutoML systems with less time cost.