Super Greedy Trees

超级贪婪树

• 批准号: 10669107
• 负责人: Hemant Ishwaran
• 金额: $ 42.21万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10669107
• 关键词: Big Data; Computer software; Data; Data Analyses; Machine Learning; National Institute of General Medical Sciences; Performance; Research Personnel; Structure; Survival Analysis; Time; Trees; base; classification trees; open source; regression trees; unsupervised learning

Project Summary/Abstract We identify critical weaknesses with Classification and Regression Trees (CART), a widely used base learner for machine learning of big omic-data analysis, and propose to replace these with a fundamentally different type of base learner we call super greedy trees (SGT's). SGT's cut the space in a fundamentally different manner, resulting in a richer partition structure with provable consistency and superior empirical performance. The project will develop a unified SGT framework for big data analysis using machine learning including the treatment of time varying covariate survival analysis, unsupervised learning, highly imbalanced data and multivariate regression. The SGT framework will be deployed within scalable and extensible open source software that will allow NIGMS researchers to deploy them to deal with their challenging big data problems.

项目摘要/摘要 我们确定分类和回归树（CART）的关键弱点，这是一个广泛使用的基础学习者 用于大型Omic-DATA分析的机器学习，并提出用根本不同类型替换它们的建议 在基础学习者中，我们称为超级贪婪的树（Sgt's）。中士以根本不同的方式削减了空间， 导致具有可证明的一致性和出色的经验表现的富划分结构。项目 将开发一个使用机器学习的大数据分析的统一的SGT框架，包括时间 不同的协变量生存分析，无监督的学习，高度不平衡的数据和多元回归。 SGT框架将部署在可扩展且可扩展的开源软件中，该软件将允许Nigms 研究人员部署他们以应对他们的挑战大数据问题。

项目成果

Commentary: To classify means to choose a threshold.

• DOI: 10.1016/j.jtcvs.2021.08.009
• 发表时间: 2023-04
• 期刊: The Journal of thoracic and cardiovascular surgery
• 作者: Lyu J;Ishwaran H
• 通讯作者: Ishwaran H