Tree Ensemble Regression and Classification Methods

树集成回归和分类方法

• 批准号: 6832086
• 负责人: JAMES SCHIMERT
• 金额: $ 9.99万
• 依托单位: INSIGHTFUL CORPORATION
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2005-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6832086
• 关键词: clinical research; computer assisted medical decision making; computer graphics /printing; computer human interaction; computer program /software; computer simulation; computer system design /evaluation; human data; mathematical model; method development; model design /development; neoplasm /cancer classification /staging; neoplasm /cancer diagnosis; neoplasm /cancer remission /regression; prognosis; statistics /biometry

DESCRIPTION (provided by applicant): This SBIR aims to produce next generation classification and regression software based upon ensembles of decision trees: bagging, random forests, and boosting. The prediction accuracy of these methods has caused much excitement in the machine learning community, and both challenges and complements the data modeling culture prevalent among biostatisticians. Recent research extends the methodology to likelihood based methods used in biostatistics, leading to models for survival data and generalized forest models. Generalized forest models extend regression forests in the same way that generalized linear models extend linear models. This software would apply broadly, including to medical diagnosis, prognostic modeling, and detecting cancer; and for modeling patient characteristics like blood pressure, discrete responses in clinical trials, and count data. Phase I work will prototype software for survival data, and investigate the performance of ensemble methods on simulated and real data. For survival applications, we will assess out-of-bag estimates of performance, and investigate measures of variable importance and graphics that help clinicians understand the results. Experience writing prototypes and using them on data will lead to a preliminary software design that serves as the foundation of Phase II work. Phase II will expand upon this work to create commercial software. We will research and implement algorithms for a wider range of applications including generalized forest models, classification, and least squares regression. We will also implement robust loss criteria that enable good performance on noisy data, and make adaptations to handle large data sets. This proposed software will enable medical researchers to obtain high prediction accuracy, and complement traditional tools like discriminant analysis, linear and logistic regression models, and the Cox model.

描述（由申请人提供）： 该SBIR旨在基于决策树的集合来生产下一代分​​类和回归软件：包装，随机森林和增强。这些方法的预测准确性引起了机器学习社区的极大兴奋，并且挑战和补充了生物统计学家普遍存在的数据建模文化。最近的研究将方法扩展到了生物统计学中使用的基于可能性的方法，从而导致生存数据和广义森林模型的模型。广义森林模型以与广义线性模型扩展线性模型相同的方式扩展了回归森林。 该软件将广泛应用，包括医学诊断，预后建模和检测癌症；用于对患者特征进行建模，例如血压，临床试验中的离散反应和计数数据。 第一阶段的工作将原型软件用于生存数据，并研究模拟和真实数据的集合方法的性能。对于生存应用，我们将评估绩效的淘汰外估计，并研究可变重要性和图形的度量，以帮助临床医生理解结果。经验编写原型并在数据上使用它们将导致初步软件设计，该设计是II期工作的基础。 第二阶段将扩展这项工作以创建商业软件。我们将研究和实施用于更广泛的森林模型，分类和最小二乘回归在内的更广泛应用的算法。我们还将实施强大的损失标准，以在嘈杂的数据上实现良好的性能，并进行改编以处理大型数据集。 该提出的软件将使医学研究人员能够获得高预测的准确性，并补充传统工具，例如判别分析，线性和逻辑回归模型以及COX模型。