Auto-CASH：使用深度Q网络的自主分类算法选择
Auto-CASH: Autonomous Classification Algorithm Selection with Deep Q-Network
摘要：
各种数据源产生的大量数据集对机器学习算法选择和超参数配置提出了挑战。对于特定的机器学习任务，领域专家通常需要花费大量时间来选择合适的算法并配置其超参数。如果能够自动解决算法选择和超参数优化问题，将在保证性能的前提下更高效地执行任务。这种问题也被称为现金问题。早期的工作要么需要大量的人力劳动，要么要承受很高的时间或空间复杂度。在我们的工作中，我们提出了Auto-CASH，一个基于元学习的预训练模型，以更有效地解决CASH问题。Auto-CASH是第一种利用Deep Q-Network为每个数据集自动选择元特征的方法，从而在不引入太多人力的情况下大大减少了时间成本。为了证明我们的模型的有效性，我们在120个真实世界的分类数据集上进行了广泛的实验。实验结果表明，与传统的CASH算法相比，Auto-CASH算法在更短的时间内取得了更好的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
