Bias-Corrected Hierarchical Bayesian Classification With a Selected Subset of High-Dimensional Features
基于高维特征筛选子集的偏差校正层级贝叶斯分类
Class prediction based on high-dimensional features has received a great deal of attention in many areas of application. For example, biologists are interested in using microarray gene expression profiles for diagnosis or prognosis of a certain disease (e.g., cancer). For computational and other reasons, it is necessary to select a subset of features before fitting a statistical model, by evaluating how strongly the features are related to the response. However, such a…