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Novel Semi-Supervised Learning Models to Balance Data Inclusivity and Usability in Healthcare Applications

Abstract Semi-supervised learning (SSL) is sub-field of statistical machine learning that is useful for problems that involve having only a few labeled instances with predictor (X) and target (Y) information, and abundance of unlabeled instances that only have predictor (X) information. SSL harnesses the target information available in the limited labeled data, as well as the information in the abundant unlabeled data to build strong predictive models. However, not all the included information is useful. For example, some features may correspond to noise and including them will hurt the predictive model performance. Additionally, some instances may not be as relevant to model building and their inclusion will increase training time and potentially... (more)
Created Date 2019
Contributor Gaw, Nathan (Author) / Li, Jing (Advisor) / Wu, Teresa (Committee member) / Yan, Hao (Committee member) / Hu, Leland (Committee member) / Arizona State University (Publisher)
Subject Industrial engineering / Biomedical engineering / Bioinformatics / glioblastoma / graph sampling / migraine / particle swarm optimization / semi-supervised learning / telemonitoring
Type Doctoral Dissertation
Extent 163 pages
Language English
Note Doctoral Dissertation Industrial Engineering 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis