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.

项目总结/文摘