通过扩展特征模型的建模质量和机器学习管道
Modeling Quality and Machine Learning Pipelines through Extended Feature Models
摘要：
最近机器学习（ML）方法的复杂性增加，导致有必要减轻研究和工业开发过程。ML管道已经成为许多领域的专家、数据科学家和研究人员的重要工具，使他们能够轻松地将多个ML模型组合在一起，以覆盖从原始数据集开始的完整分析过程。多年来，已经提出了几种解决方案来自动构建ML管道，其中大多数专注于输入数据集的语义方面和特征。然而，考虑到ML系统所需的新质量问题（如公平性，可解释性，隐私等）的方法。仍然下落不明在本文中，我们首先从文献中确定ML系统的关键质量属性。此外，我们提出了一种新的工程方法，通过适当地扩展特征模型的元模型的质量ML管道。所提出的方法允许对ML管道、其质量要求（整个管道和单个阶段）以及用于实现每个管道阶段的算法的质量特性进行建模。最后，我们证明了我们的模型考虑分类问题的表现力。
英文摘要：
The recently increased complexity of Machine Learning (ML) methods, led to the necessity to lighten both the research and industry development processes. ML pipelines have become an essential tool for experts of many domains, data scientists and researchers, allowing them to easily put together several ML models to cover the full analytic process starting from raw datasets. Over the years, several solutions have been proposed to automate the building of ML pipelines, most of them focused on semantic aspects and characteristics of the input dataset. However, an approach taking into account the new quality concerns needed by ML systems (like fairness, interpretability, privacy, etc.) is still missing. In this paper, we first identify, from the literature, key quality attributes of ML systems. Further, we propose a new engineering approach for quality ML pipeline by properly extending the Feature Models meta-model. The presented approach allows to model ML pipelines, their quality requirements (on the whole pipeline and on single phases), and quality characteristics of algorithms used to implement each pipeline phase. Finally, we demonstrate the expressiveness of our model considering the classification problem.
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。
