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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 ****