Recursive partitioning and ensemble methods for classifying an ordinal response

描述(由申请人提供)： 应用于微阵列数据的分类方法在很大程度上是由机器学习团体开发的，因为大的p(协变量数量)问题是高通量基因组实验所固有的。随机森林(RF)方法已被证明与其他机器学习方法(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方法的一个明显优势是该算法提供了可变的重要性度量。因此，人们可以评估每个基因在预测模型中的相对重要性。在大量的应用中，被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，分类为完全缓解、部分缓解、稳定和进展性疾病。这些回答是有序的；虽然回答之间有内在的顺序，但它们之间没有已知的潜在数字关系。虽然可以将标准名义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。由于在机器学习文献中，顺序分类方法在很大程度上被忽略，因此本建议的具体目标是：(1)通过为R编程环境开发计算工具来扩展用于预测顺序响应的递归划分和RF方法；(2)使用模拟、基准和基因表达数据集相对于其他方法来评估所提出的顺序分类方法；(3)开发和评估当对顺序响应感兴趣时评估变量重要性的方法。提出了一种新的分类树生长分裂准则和变量重要度估计方法，较好地考虑了有序响应的性质。此外，将在集成学习框架下研究广义基尼指数和有序两种方法，这是以前没有进行过的。该项目对科学界具有重要意义，因为该项目提供的有序分类方法将广泛适用于各种健康、社会和行为研究领域，这些领域通常收集有序的回答。

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.