Fast flexible feature selection for high dimensional challenging data

针对高维挑战性数据快速灵活的特征选择

• 批准号: DP210100521
• 负责人: A/Prof John Ormerod
• 金额: $ 27.43万
• 依托单位: The University of Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2021
• 资助国家: 澳大利亚
• 起止时间: 2021-11-01 至 2024-10-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP210100521
• 关键词: Fast; flexible; feature; selection; dimensional

The project aims to provide new frameworks for fast flexible feature selection and appropriate modelling of heterogeneous data through structural varying-coefficient regression models. The outcomes will be a series of new statistical methods and concepts enabling more powerful modelling of complex bioscience data. The project will create the science for building reliable statistical models taking model uncertainty into account, impacting how results will be interpreted, and with accompanying software. This will be a significant improvement in the assessment of model confidence in the food and health research priority areas including areas such as meat science, Huntington’s disease, and kidney transplantation.

该项目旨在通过结构性变系数回归模型，为快速、灵活的特征选择和不同类型数据的适当建模提供新的框架。其结果将是一系列新的统计方法和概念，使复杂的生物科学数据能够建立更强大的模型。该项目将为建立可靠的统计模型创造科学，同时考虑到模型的不确定性，影响如何解释结果，并使用附带的软件。这将是对食品和健康研究优先领域的模型信心评估的重大改进，包括肉类科学、亨廷顿病和肾脏移植等领域。