Hyperparameter recommendation via automated meta-feature selection embedded with kernel group Lasso learning

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Liping Deng , MingQing Xiao
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引用次数: 0

Abstract

Hyperparameter recommendation via meta-learning relies on the characterization and quality of meta-features. These meta-features provide critical information about the underlying datasets but are often selected manually based on the practitioner’s experience and preference, which can be inefficient and ineffective in many applications. In this paper, we propose a novel hyperparameter recommendation approach that integrates with a Lasso-based multivariate kernel group (KGLasso) model. The developed KGLasso model automatically identifies primary meta-features through model training. By selecting the most explanatory meta-features for a specific meta-learning task, the recommendation performance becomes much more effective. Our KGLasso model builds on a group-wise generalized multivariate Lasso approach. Within this framework, we establish a minimization algorithm using a corresponding auxiliary function, which is mathematically proven to be convergent and robust. As an application, we develop a hyperparameter recommendation system using our built KGLasso model on 120 UCI datasets for the well-known support vector machine (SVM) algorithm. This system efficiently provides competent hyperparameter recommendations for new tasks. Extensive experiments, including comparisons with popular meta-learning baselines and search algorithms, demonstrate the superiority of our proposed approach. Our results highlight the benefits of integrating model learning and feature selection to construct an automated meta-learner for hyperparameter recommendation in meta-learning.
通过嵌入核群拉索学习的自动元特征选择推荐超参数
通过元学习推荐超参数依赖于元特征的特性和质量。这些元特征提供了底层数据集的关键信息,但通常是根据实践者的经验和偏好手动选择的,这在许多应用中可能是低效和无效的。在本文中,我们提出了一种新颖的超参数推荐方法,该方法与基于拉索的多元核群(KGLasso)模型相结合。所开发的 KGLasso 模型可通过模型训练自动识别主要元特征。通过为特定元学习任务选择最具解释力的元特征,推荐性能会变得更加有效。我们的 KGLasso 模型建立在分组广义多元 Lasso 方法的基础上。在此框架内,我们使用相应的辅助函数建立了最小化算法,该算法经数学证明具有收敛性和鲁棒性。在应用中,我们利用所建立的 KGLasso 模型,在 120 个 UCI 数据集上为著名的支持向量机(SVM)算法开发了一个超参数推荐系统。该系统能有效地为新任务提供胜任的超参数推荐。广泛的实验,包括与流行的元学习基线和搜索算法的比较,证明了我们提出的方法的优越性。我们的研究结果凸显了整合模型学习和特征选择来构建元学习中超参数自动推荐元学习器的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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