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.
翻译:自动机器学习(Automal Learning) 已经出现一个自动机器学习(AutomaML), 处理特定学习任务的算法的选择和配置。 随着自动ML的逐步发展, 引入了几种有效的方法, 特别是传统分类和回归问题。 除了自动ML成功之外, 有几个问题仍未解决。 其中一个问题是自动ML方法缺乏处理不同类型数据的能力。 基于这个假设, 本文采用自动ML( AutomaML) 方法解决多标签分类问题。 在刚果解放运动, 每一个例子都可以同时与几个类标签挂钩, 与标准分类任务不同, 标准分类任务只有一个等级标签。 在这项工作中, 我们提供了五种自动化多标签分类方法的一般比较 -- -- 两种进化方法、一种巴伊斯优化方法、一种随机搜索和一种贪婪搜索方法 -- -- 以14个数据集和三个设计搜索空间为基础。 总的来说, 我们观察到最突出的方法是基于基于粗俗语语的基因编程(GGGGP) 的搜索方法, 即Autouto-MEKA$GGGP}} GGPAut- $GGP$, $, 。 Autal-MEGGGGP$GGP}] gGP} 。 在统计搜索方法中, 提供了除所有统计方法中的最佳平均搜索方法中的最佳搜索方法, 除了统计方法之外, 在统计方法比其他方法中的最佳搜索结果。