自动机器学习(AutoML)是将机器学习应用于实际问题的过程的自动化过程。AutoML涵盖了从原始数据集到可部署的机器学习模型的完整管道。提出将AutoML作为基于人工智能的解决方案来应对不断增长的应用机器学习的挑战。 AutoML的高度自动化允许非专家使用机器学习模型和技术,而无需首先成为该领域的专家。 从机器学习角度讲,AutoML 可以看作是一个在给定数据和任务上学习和泛化能力非常强大的系统。但是它强调必须非常容易使用;从自动化角度讲,AutoML 则可以看作是设计一系列高级的控制系统去操作机器学习模型,使得模型可以自动化地学习到合适的参数和配置而无需人工干预。

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论文链接:https://arxiv.org/pdf/2003.03384.pdf

GitHub 项目地址:https://github.com/google-research/google-research/tree/master/automl_zero#automl-zero

在这项研究中,谷歌提出了新方法 AutoML-Zero,该方法从空程序(empty program)开始,仅使用基本的数学运算作为构造块,使用进化方法来自动找出完整 ML 算法的代码。

在一些小的图像分类问题上,AutoML-Zero 方法重新发现了一些基本的 ML 技巧,如具备反向传播的双层神经网络和线性回归等,而这些是之前的研究人员经过数年时间才发现的。

这一结果表明,自动发现更新颖的 ML 算法以解决更棘手的问题,这一想法在未来是可行的。

接下来,我们来看 AutoML-Zero 方法的具体实现原理。

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Automated Machine Learning (AutoML) has emerged to deal with the selection and configuration of algorithms for a given learning task. With the progression of AutoML, several effective methods were introduced, especially for traditional classification and regression problems. Apart from the AutoML success, several issues remain open. One issue, in particular, is the lack of ability of AutoML methods to deal with different types of data. Based on this scenario, this paper approaches AutoML for multi-label classification (MLC) problems. In MLC, each example can be simultaneously associated to several class labels, unlike the standard classification task, where an example is associated to just one class label. In this work, we provide a general comparison of five automated multi-label classification methods -- two evolutionary methods, one Bayesian optimization method, one random search and one greedy search -- on 14 datasets and three designed search spaces. Overall, we observe that the most prominent method is the one based on a canonical grammar-based genetic programming (GGP) search method, namely Auto-MEKA$_{GGP}$. Auto-MEKA$_{GGP}$ presented the best average results in our comparison and was statistically better than all the other methods in different search spaces and evaluated measures, except when compared to the greedy search method.

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