Automatic workflow composition (AWC) is a relevant problem in automated
machine learning (AutoML) that allows finding suitable sequences of
preprocessing and prediction models together with their optimal
hyperparameters. This problem can be solved using evolutionary algorithms and,
in particular, grammar-guided genetic programming (G3P). Current G3P approaches
to AWC define a fixed grammar that formally specifies how workflow elements can
be combined and which algorithms can be included. In this paper we present
\ourmethod, an interactive G3P algorithm that allows users to dynamically
modify the grammar to prune the search space and focus on their regions of
interest. Our proposal is the first to combine the advantages of a G3P method
with ideas from interactive optimisation and human-guided machine learning, an
area little explored in the context of AutoML. To evaluate our approach, we
present an experimental study in which 20 participants interact with \ourmethod
to evolve workflows according to their preferences. Our results confirm that
the collaboration between \ourmethod and humans allows us to find
high-performance workflows in terms of accuracy that require less tuning time
than those found without human intervention.

自动工作流合成是自动化机器学习中的一个相关问题，通过进化算法和语法引导的遗传规划方法，将预处理和预测模型的最佳超参数与适当的顺序相结合。然而，我们的方法是第一个将 G3P 方法的优点与交互式优化和人类指导的机器学习的思想相结合的提案，通过与人类的合作，我们发现在准确性方面具有高性能且需要较少的调整时间的工作流程。

通过交互式 AutoML 演化的机器学习工作流程

Evolving machine learning workflows through interactive AutoML

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[..]

自动机器学习，自动工作流组合，EvoFlow，基因操作符，集成