A0346
Title: Network augmented classification
Authors: Ji Zhu - University of Michigan (United States) [presenting]
Abstract: In classical classification, a data point is classified given its individual covariates. Often, additional network information describing the connectivity relationships between points are also available, which in principle can be used to improve classification performance. We develop a general statistical framework for network augmented classification. Under this framework, we derive the optimal Bayes classifiers for two general families of distributions incorporating both covariates and networks, one being generative and the other being discriminative. Further, we establish consistency results for plug-in classifiers with respect to the optimal classifiers under the generative and discriminative families,respectively. We also apply the general approaches to two specific models and propose two effective classification methods for practical use. The proposed methods have been evaluated using both simulation studies and real-world data examples, and the results are promising.
