通过多目标和多信息源贝叶斯优化实现公平和绿色超参数优化

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0

摘要

摘要 最近有人指出,在寻找最佳机器学习模型时只关注准确性会放大数据中的偏差,从而导致不公平的预测和决策支持。最近,有人提出了多目标超参数优化方法,以寻找在准确性和公平性之间提供同等帕累托效率权衡的机器学习模型。虽然这些方法被证明比公平感知的机器学习算法更具通用性--这些算法在优化准确性的同时也会限制公平性的某些阈值--但由于在大型数据集的情况下需要消耗大量能源,它们的碳足迹可能会非常惊人。我们提出了一种名为 FanG-HPO 的方法:基于多目标和多信息源贝叶斯优化的公平绿色超参数优化(HPO)。FanG-HPO 利用大型数据集的子集来获得准确性和公平性的廉价近似值(又称信息源),并利用多目标贝叶斯优化来有效识别帕累托效率(准确性和公平性)机器学习模型。实验考虑了四个基准(公平性)数据集和四种机器学习算法,并对照公平性感知机器学习方法和两种解决多目标和能量感知优化问题的最先进贝叶斯优化工具,对 FanG-HPO 进行了评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fair and green hyperparameter optimization via multi-objective and multiple information source Bayesian optimization

Abstract

It has been recently remarked that focusing only on accuracy in searching for optimal Machine Learning models amplifies biases contained in the data, leading to unfair predictions and decision supports. Recently, multi-objective hyperparameter optimization has been proposed to search for Machine Learning models which offer equally Pareto-efficient trade-offs between accuracy and fairness. Although these approaches proved to be more versatile than fairness-aware Machine Learning algorithms—which instead optimize accuracy constrained to some threshold on fairness—their carbon footprint could be dramatic, due to the large amount of energy required in the case of large datasets. We propose an approach named FanG-HPO: fair and green hyperparameter optimization (HPO), based on both multi-objective and multiple information source Bayesian optimization. FanG-HPO uses subsets of the large dataset to obtain cheap approximations (aka information sources) of both accuracy and fairness, and multi-objective Bayesian optimization to efficiently identify Pareto-efficient (accurate and fair) Machine Learning models. Experiments consider four benchmark (fairness) datasets and four Machine Learning algorithms, and provide an assessment of FanG-HPO against both fairness-aware Machine Learning approaches and two state-of-the-art Bayesian optimization tools addressing multi-objective and energy-aware optimization.

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来源期刊
Machine Learning
Machine Learning 工程技术-计算机:人工智能
CiteScore
11.00
自引率
2.70%
发文量
162
审稿时长
3 months
期刊介绍: Machine Learning serves as a global platform dedicated to computational approaches in learning. The journal reports substantial findings on diverse learning methods applied to various problems, offering support through empirical studies, theoretical analysis, or connections to psychological phenomena. It demonstrates the application of learning methods to solve significant problems and aims to enhance the conduct of machine learning research with a focus on verifiable and replicable evidence in published papers.
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