Title
Large Margin Hierarchical Classification
Speaker
Xiaotong Shen, University of Minnesota
Abstract
In hierarchical classification, class labels are structured in that each label value corresponds to one non-root node in a tree, where the inter-class relationship for classification is specified by directed paths of the tree. In this talk, I will present a large margin method for hierarchical classification. The main focus here is to utilize the dependency structure among classes to improve the classification performance of flat classification. In such a situation, flat classification is infeasible in the presence of a large number of dependent classes, which occurs often in gene function discovery. Various hierarchical losses will be discussed, in addition to an application to gene function prediction.
This work is joint with H. Wang and W. Pan.
