Feature reduction improves classification accuracy in healthcare
- UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
- Hamid R. Nemati, Professor (Creator)
- Fereidoon "Fred" Sadri, Professor (Creator)
- Institution
- The University of North Carolina at Greensboro (UNCG )
- Web Site: http://library.uncg.edu/
Abstract: Our work focuses on inductive transfer learning, a setting in which one assumes that both source and target tasks share the same features and label spaces. We demonstrate that transfer learning can be successfully used for feature reduction and hence for more efficient classification performance. Further, our experiments show that this approach increases the precision of the classification task as well.
Feature reduction improves classification accuracy in healthcare
PDF (Portable Document Format)
609 KB
Created on 4/15/2020
Views: 1126
Additional Information
- Publication
- IDEAS 2018: Proceedings of the 22nd International Database Engineering & Applications Symposium, June 2018. Pages 193–198
- Language: English
- Date: 2018
- Keywords
- classification, transfer learning, feature reduction, classification accuracy