基于语法的进化方法用于自动化工作流组合，结合领域特定算子与集成多样性
Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity
AI总结：
针对AutoML中工作流组合（AWC）的算法组合受限问题，提出基于语法的进化方法EvoFlow，通过领域特定遗传算子和多样性更新机制优化工作流结构与超参数，实验证明其性能显著优于现有方法。
摘要：
从原始数据中提取有价值和新颖见解的过程涉及一系列复杂步骤。在自动化机器学习（AutoML）领域，一个重要的研究焦点是自动化该过程的各个方面，特别是诸如算法选择和超参数优化等任务。AutoML中一个特别具有挑战性的任务是自动工作流组合（AWC）。AWC旨在为特定数据集确定最有效的数据预处理和机器学习算法序列，并搭配其最佳超参数。然而，现有的AWC方法在工作流中组合算法的数量和方式上受到限制。为解决这一不足，本文介绍了EvoFlow，一种基于语法的进化方法，用于AWC。EvoFlow增强了工作流结构设计的灵活性，使实践者能够选择最适合其特定需求的算法。EvoFlow通过整合两个创新特性而脱颖而出。首先，它采用一套专为AWC设计的遗传算子，以优化工作流的结构及其超参数。其次，它实现了一种新颖的更新机制，以丰富不同工作流所做的预测的多样性。促进这种多样性有助于防止算法过拟合。为此，EvoFlow构建了一个集成，其工作流在错误分类的实例上有所不同。为了评估EvoFlow的有效性，我们使用一组分类基准进行了实证验证。我们首先进行消融研究，以展示EvoFlow独特组件带来的性能提升。然后，我们将EvoFlow与其他AWC方法进行比较，涵盖进化和非进化技术。我们的研究结果表明，EvoFlow的专门遗传算子和更新机制显著优于当前领先的方法[..]。
英文摘要：
The process of extracting valuable and novel insights from raw data involves a series of complex steps. In the realm of Automated Machine Learning (AutoML), a significant research focus is on automating aspects of this process, specifically tasks like selecting algorithms and optimising their hyper-parameters. A particularly challenging task in AutoML is automatic workflow composition (AWC). AWC aims to identify the most effective sequence of data preprocessing and ML algorithms, coupled with their best hyper-parameters, for a specific dataset. However, existing AWC methods are limited in how many and in what ways they can combine algorithms within a workflow. Addressing this gap, this paper introduces EvoFlow, a grammar-based evolutionary approach for AWC. EvoFlow enhances the flexibility in designing workflow structures, empowering practitioners to select algorithms that best fit their specific requirements. EvoFlow stands out by integrating two innovative features. First, it employs a suite of genetic operators, designed specifically for AWC, to optimise both the structure of workflows and their hyper-parameters. Second, it implements a novel updating mechanism that enriches the variety of predictions made by different workflows. Promoting this diversity helps prevent the algorithm from overfitting. With this aim, EvoFlow builds an ensemble whose workflows differ in their misclassified instances. To evaluate EvoFlow's effectiveness, we carried out empirical validation using a set of classification benchmarks. We begin with an ablation study to demonstrate the enhanced performance attributable to EvoFlow's unique components. Then, we compare EvoFlow with other AWC approaches, encompassing both evolutionary and non-evolutionary techniques. Our findings show that EvoFlow's specialised genetic operators and updating mechanism substantially outperform current leading methods[..]
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。
