Classifier Chains for Multi-Label Classification with Incomplete Labels

Graduate Project

Pubblico Deposited

Citeable URL: https://ir.library.oregonstate.edu/concern/graduate_projects/fx719r653

Descriptions

Attribute Name	Values
Creator	Almuallim, Jafer
Abstract	Many methods have been explored in the literature of multi-label learning, ranging from simple problem transformation to more complex method that capture correlation among labels. However, mostly all existing works do not address the challenge with incomplete label data. The goal of this project is to extend the work of ensemble classifier chain to learn models using training examples with incomplete label assignment. This scenario is highly expected in many real-world application. For example, in image annotation, a user provides partial tags, or label assignment, for the image. We propose a new method that consider the multi-label learning problem in which portion of label assignment is missing. A further evaluation is covered in this project to study the effect of different parameters accompany this approach.
Resource Type	Capstone Project
Date Available	2013-07-08T23:48:45+00:00
Date Issued	2013-06-12
Degree Level	Master's
Degree Name	Master of Science (M.S.)
Degree Field	Computer Science
Degree Grantor	Oregon State University
Commencement Year	2013
Advisor	Fern, Xiaoli
Committee Member	Raich, Raviv Wong, Weng-Keen
Academic Affiliation	Electrical Engineering and Computer Science
Dichiarazione dei diritti	In Copyright
Publisher	Oregon State Unversity
Peer Reviewed	No
Language	English [eng]
Replaces	http://hdl.handle.net/1957/40135

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	final_project_report_-_Jafer_Almuallim.pdf	2017-10-05	Pubblico	Scaricare