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由机器学习社区开发的方法，因为大 p（协变量数量）问题是高通量基因组实验中固有的。随机森林 (RF) 方法已被证明与其他机器学习方法（例如神经网络和支持向量机）相比具有竞争力。除了提高准确性之外，与大多数机器学习方法相比，RF 方法的一个明显优势是算法提供了可变重要性度量。因此，我们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，要预测的类别可能本质上是有序的。顺序反应的例子包括 TNM 分期（I、II、III、IV）；药物毒性（无、轻度、中度、重度）；或对治疗的反应分为完全反应、部分反应、疾病稳定和疾病进展。这些反应是有序的；虽然响应之间存在固有的顺序，但它们之间不存在已知的潜在数字关系。虽然人们可以将标准标称响应方法应用于有序响应数据，但这样做会丢失数据中固有的有序信息。由于序数分类方法在机器学习文献中很大程度上被忽视，因此该提案的具体目标是（1）通过开发 R 编程环境的计算工具来扩展递归分区和 RF 方法来预测序数响应； (2) 使用模拟、基准和基因表达数据集对照替代方法评估所提出的序数分类方法； (3) 当兴趣在于预测顺序响应时，开发和评估评估变量重要性的方法。提出了分类树生长的新分裂标准和估计变量重要性的方法，适当地考虑了序数响应的性质。此外，还将在集成学习框架下研究广义基尼指数和有序二元方法，这是以前没有进行过的。该项目对科学界意义重大，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，这些领域通常收集序数尺度的响应。