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主要是由机器学习社区开发的，因为大p（协变量数）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）。除了提高精度外，RF方法与大多数机器学习方法相比的一个明显优势是算法提供了变量重要性度量。因此，可以评估每个基因对预测模型的相对重要性。在大量的应用中，要预测的类可以是固有的序数。顺序响应的实例包括TNM分期（I、II、III、IV）;药物毒性（无、轻度、中度、重度）;或对治疗的响应，分类为完全响应、部分响应、稳定疾病和进行性疾病。这些反应是有序的;虽然在这些响应之间存在固有的排序，但它们之间没有已知的基本数字关系。虽然人们可以将标准的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。由于顺序分类方法在机器学习文献中被很大程度上忽视，因此该提案的具体目标是（1）通过开发用于R编程环境的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来评估所提出的顺序分类方法与替代方法;（3）开发和评估方法，以评估变量的重要性时，感兴趣的是在预测一个有序的反应。提出了新的分类树生长的分裂准则和估计变量重要性的方法，这些准则适当地考虑了有序响应的性质。此外，广义基尼指数和有序两种方法将在集成学习框架下进行研究，这是以前没有进行过的。该项目对科学界具有重要意义，因为从该项目中获得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规模的反应。

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.