Classifier Chains for Multi-Label Classification with Incomplete Labels Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_projects/fx719r653

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  • 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.
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  • description.provenance : Made available in DSpace on 2013-07-08T23:48:45Z (GMT). No. of bitstreams: 1 final project report - Jafer Almuallim.pdf: 349185 bytes, checksum: f04862d890f48c4746994f25c5675c41 (MD5) Previous issue date: 2013-06-12
  • description.provenance : Submitted by Jafer Almuallim (almuallj@onid.orst.edu) on 2013-06-24T20:10:43Z No. of bitstreams: 1 final project report - Jafer Almuallim.pdf: 349185 bytes, checksum: f04862d890f48c4746994f25c5675c41 (MD5)
  • description.provenance : Approved for entry into archive by Sue Kunda(sue.kunda@oregonstate.edu) on 2013-07-08T23:48:45Z (GMT) No. of bitstreams: 1 final project report - Jafer Almuallim.pdf: 349185 bytes, checksum: f04862d890f48c4746994f25c5675c41 (MD5)

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