Recursive partitioning and ensemble methods for classifying an ordinal response

用于对序数响应进行分类的递归划分和集成方法

• 批准号: 7670456
• 负责人: Kellie J. Archer
• 金额: $ 7.48万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-15 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7670456
• 关键词: Algorithms; Behavioral Research; Benchmarking; Biological Neural Networks; Classification; Communities; Data; Data Analyses; Data Set; Discriminant Analysis; Drug toxicity; Environment; Gene Expression; Genes; Genomics; Goals; Health; Health Surveys; Image Analysis; In complete remission; Individual; Learning; Literature; Machine Learning; Measures; Methodology; Methods; Modeling; Nature; Neoplasm Metastasis; Northern Blotting; Outcome; Performance; Process; Progressive Disease; Relative (related person); Simulate; Stable Disease; Staging; Structure; Technology; Time; Trees; computerized tools; forest; improved; indexing; interest; neglect; novel; partial response; predictive modeling; programs; research study; response; social; tumor

DESCRIPTION (provided by applicant): Classification methods applied to microarray data have largely been those developed by the machine learning community, since the large p (number of covariates) problem is inherent in high-throughput genomic experiments. The random forest (RF) methodology has been demonstrated to be competitive with other machine learning approaches (e.g., neural networks and support vector machines). Apart from improved accuracy, a clear advantage of the RF method in comparison to most machine learning approaches is that variable importance measures are provided by the algorithm. Therefore, one can assess the relative importance each gene has on the predictive model. In a large number of applications, the class to be predicted may be inherently ordinal. Examples of ordinal responses include TNM stage (I,II,III, IV); drug toxicity (none, mild, moderate, severe); or response to treatment classified as complete response, partial response, stable disease, and progressive disease. These responses are ordinal; while there is an inherent ordering among the responses, there is no known underlying numerical relationship between them. While one can apply standard nominal response methods to ordinal response data, in so doing one loses the ordered information inherent in the data. Since ordinal classification methods have been largely neglected in the machine learning literature, the specific aims of this proposal are to (1) extend the recursive partitioning and RF methodologies for predicting an ordinal response by developing computational tools for the R programming environment; (2) evaluate the proposed ordinal classification methods against alternative methods using simulated, benchmark, and gene expression datasets; (3) develop and evaluate methods for assessing variable importance when interest is in predicting an ordinal response. Novel splitting criteria for classification tree growing and methods for estimating variable importance are proposed, which appropriately take the nature of the ordinal response into consideration. In addition, the Generalized Gini index and ordered twoing methods will be studied under the ensemble learning framework, which has not been previously conducted. This project is significant to the scientific community since the ordinal classification methods to be made available from this project will be broadly applicable to a variety of health, social, and behavioral research fields, which commonly collect responses on an ordinal scale.

描述（由申请人提供）： 应用于微阵列数据的分类方法主要是机器学习社区开发的方法，因为大P（数量）问题是高通量基因组实验中固有的。随机森林（RF）方法已证明与其他机器学习方法（例如神经网络和支持向量机）具有竞争力。除了提高精度外，与大多数机器学习方法相比，RF方法的明显优势是，算法提供了可变的重要性度量。因此，可以评估每个基因对预测模型的相对重要性。在大量应用中，要预测的类可能是固有的。顺序反应的例子包括TNM阶段（I，II，III，IV）；药物毒性（无，轻度，中度，严重）；或对治疗的反应分类为完全反应，部分反应，稳定疾病和进行性疾病。这些反应是有序的。尽管响应之间存在固有的顺序，但它们之间没有已知的基本数值关系。虽然可以将标准名义响应方法应用于序数响应数据，但这样做会丢失数据中固有的有序信息。由于在机器学习文献中很大程度上忽略了序数分类方法，因此该提案的具体目的是（1）扩展递归分区和RF方法，以通过为R编程环境开发计算工具来预测序数响应； （2）使用模拟，基准和基因表达数据集对替代方法进行替代方法评估所提出的序数分类方法； （3）在预测序数响应中的利益时，开发和评估评估可变重要性的方法。提出了针对分类树生长的新型分裂标准和估计可变重要性的方法，这些方法适当考虑了序数响应的性质。此外，将在整体学习框架下研究广义的Gini指数和有序的两种方法，该框架以前尚未进行。该项目对科学界很重要，因为该项目提供的序数分类方法将广泛适用于各种健康，社会和行为研究领域，这些方法通常会以序数为单位。