Classification: methodology for variable selection and efficient tuning and comparasion of models

分类：变量选择和模型高效调整和比较的方法

• 批准号: 36462-2008
• 负责人: Welch, William
• 金额: $ 1.82万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=450618
• 关键词: Classification; methodology; variable; selection; efficient

Complex, high-dimensional data arise in many important classification problems. For example, in drug discovery, drug-like molecules are characterized by hundreds or thousands of chemical descriptor variables. The classification problem is to model the relationship between the chemical descriptors and the active/inactive assay response desired of the drug being developed, e.g., binding to a particular enzyme. Another data challenge is that active compounds are very rare in the assay data used to train models (the rare-class of interest problem).

在许多重要的分类问题中，都会出现复杂的高维数据。例如，在药物发现中，药物样分子的特征在于数百或数千个化学描述符变量。分类问题是对化学描述符与所开发药物所需的活性/非活性测定响应之间的关系进行建模，例如，与特定的酶结合。另一个数据挑战是，活性化合物在用于训练模型的测定数据中非常罕见（感兴趣的稀有类问题）。