变异性感知的机器学习模型选择：特征建模、实例化与实验案例研究
Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study
AI总结：
本文针对机器学习模型选择过程缺乏系统性软件工程设计支持的问题，提出一种变异性感知的ML算法选择方法，并通过基于Scikit-Learn启发式规则的实验案例研究验证其适用性，旨在实现更明确、自适应、透明、可解释和自动化的模型选择。
摘要：
机器学习（ML）的出现引发了软件技术和构建软件应用指南的变革性转变，这些应用支持数据分析过程活动，如数据摄取、建模和部署。具体而言，这一转变正在影响ML模型选择，而模型选择是该过程中的关键阶段之一。从核心ML方法的角度来看，模型选择已取得若干进展，包括基本概率度量和重采样方法。然而，从软件工程的角度来看，这种选择仍然是一个临时且非正式的过程，缺乏一种设计方法和表示形式来明确捕获选择过程，也无法支持对现有模型选择过程的规范化说明。该选择需要适应影响模型选择的各种上下文因素，例如数据特征、特征数量、预测类型及其复杂的依赖关系。此外，它没有为选择某个模型提供解释，并且在选择技术时没有考虑上下文因素及其相互依赖关系。尽管当前文献提供了各种各样的ML技术和算法，但缺乏支持算法选择的设计方法。在本文中，我们提出了一种变异性感知的ML算法选择方法，该方法考虑了模型选择过程中的共性和差异。通过一个基于Scikit-Learn启发式规则的实验案例研究说明了该方法的适用性，在该案例研究中，将文献中呈现的现有模型选择与该方法的建议选择进行了比较。所提出的方法可被视为朝着为模型选择提供更明确、自适应、透明、可解释和自动化基础迈出的一步。
英文摘要：
The emergence of machine learning (ML) has led to a transformative shift in software techniques and guidelines for building software applications that support data analysis process activities such as data ingestion, modeling, and deployment. Specifically, this shift is impacting ML model selection, which is one of the key phases in this process. There have been several advances in model selection from the standpoint of core ML methods, including basic probability measures and resampling methods. However, from a software engineering perspective, this selection is still an ad hoc and informal process, is not supported by a design approach and representation formalism that explicitly captures the selection process and can not support the specification of existing model selection procedures. The selection adapts to a variety of contextual factors that affect the model selection, such as data characteristics, number of features, prediction type, and their intricate dependencies. Further, it does not provide an explanation for selecting a model and does not consider the contextual factors and their interdependencies when selecting a technique. Although the current literature provides a wide variety of ML techniques and algorithms, there is a lack of design approaches to support algorithm selection. In this paper, we present a variability-aware ML algorithm selection approach that considers the commonalities and variations in the model selection process. The approach's applicability is illustrated by an experimental case study based on the Scikit-Learn heuristics, in which existing model selections presented in the literature are compared with selections suggested by the approach. The proposed approach can be seen as a step towards providing a more explicit, adaptive, transparent, interpretable, and automated basis for model selection.
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。
