Multilabel Classification with Label Structures

PhD Thesis Proposal Defence


Title: "Multilabel Classification with Label Structures"

by

Miss Wei BI


Abstract:

Many real-world applications involve multilabel classification, in which 
multiple labels can be associated with each sample. In many multilabel 
applications, structures exist among labels. A popular structure on labels 
is the label hierarchy, which can be achieved with the help of domain 
experts, or be automatically created from the data using procedures such 
as hierarchical clustering or Bayesian network structure learning. This 
label hierarchy may then be arranged as a tree, as in text categorization, 
or more generally, in a directed acyclic graph (DAG), as in the Gene 
Ontology used in gene functional analysis. However, current research 
efforts typically ignore such label structures or can only exploit the 
dependencies in a label tree.

In this thesis, we introduce three methods that exploit the label 
structure, either a tree or DAG, for multilabel classification. In the 
first work, we propose novel multilabel algorithms for the mandatory leaf 
node prediction problem, in which the prediction paths of a given test 
example are required to end at leaf nodes of the label hierarchy. This 
problem setting is particularly useful when the leaf nodes have much 
stronger semantic meaning than the internal nodes. In the second work, we 
discuss proper loss functions for multilabel problems when label 
hierarchies exist, and derive their corresponding Bayes-optimal 
classifiers. Thirdly, we present a probabilistic framework by 
incorporating hierarchical label constraints via posterior regularization 
such that the hierarchical constraints hold in expectation for the output 
labels during training.


Date:			Tuesday, 28 April 2015

Time:                   2:00pm - 4:00pm

Venue:                  Room 3501
                        lifts 25/26

Committee Members:	Prof. James Kwok (Supervisor)
  			Dr. Raymond Wong (Chairperson)
 			Prof. Dit-Yan Yeung
  			Prof. Nevin Zhang


**** ALL are Welcome ****