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Title: Novel approaches for hierarchical classification with case studies in protein function prediction
Author: Carlos, Nascimento Silla Junior
Awarding Body: University of Kent
Current Institution: University of Kent
Date of Award: 2011
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A very large amount of research in the data mining, machine learning, statis- tical pattern recognition and related research communities has focused on fiat classification problems. However, many problems in the real world such as hi- erarchical protein function prediction have their classes naturally organised into hierarchies. The task of hierarchical classification, however, needs to be better defined as researchers into one application domain are often unaware of similar efforts developed in other research areas. The first contribution of this thesis is to survey the task of hierarchical clas- sification across different application domains and present an unifying framework for the task. After clearly defining the problem, we explore novel approaches to the task.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available