Scikit-Learn管道的动态结构化语法演化
Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution
摘要：
机器学习（ML）模型的部署是一项困难且耗时的工作，包括一系列顺序且相关的任务，从数据预处理，设计和提取特征，到ML算法的选择及其参数化。考虑到功能的设计在许多情况下是针对具体问题的，因此需要领域专门知识，这项任务更具挑战性。为了克服这些限制，自动化机器学习（AutoML）方法寻求在很少或没有人为干预的情况下自动化管道的设计，即，自动选择必须应用于原始数据的方法序列。这些方法有可能使非专家用户能够使用ML，并为专家用户提供他们不太可能考虑的解决方案。特别是，本文描述了AutoML-DSGE -一种新的基于语法的框架，它使动态结构化语法进化（DSGE）适应Scikit-Learn分类管道的演变。实验结果包括将AutoML-DSGE与另一种基于语法的AutoML框架Resilient ClassificationPipeline Evolution（RECIPE）进行比较，并显示AutoML-DSGE生成的分类管道的平均性能始终优于RECIPE的平均性能;在10个使用的数据集中，有3个差异具有统计学意义。
英文摘要：
The deployment of Machine Learning (ML) models is a difficult and time-consuming job that comprises a series of sequential and correlated tasks that go from the data pre-processing, and the design and extraction of features, to the choice of the ML algorithm and its parameterisation. The task is even more challenging considering that the design of features is in many cases problem specific, and thus requires domain-expertise. To overcome these limitations Automated Machine Learning (AutoML) methods seek to automate, with few or no human-intervention, the design of pipelines, i.e., automate the selection of the sequence of methods that have to be applied to the raw data. These methods have the potential to enable non-expert users to use ML, and provide expert users with solutions that they would unlikely consider. In particular, this paper describes AutoML-DSGE - a novel grammar-based framework that adapts Dynamic Structured Grammatical Evolution (DSGE) to the evolution of Scikit-Learn classification pipelines. The experimental results include comparing AutoML-DSGE to another grammar-based AutoML framework, Resilient ClassificationPipeline Evolution (RECIPE), and show that the average performance of the classification pipelines generated by AutoML-DSGE is always superior to the average performance of RECIPE; the differences are statistically significant in 3 out of the 10 used datasets.
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。
