Skip to content
VuFind
Advanced
  • Improving Accuracy of Imbalanc...
  • Cite this
  • Print
  • Export Record
    • Export to RefWorks
    • Export to EndNoteWeb
    • Export to EndNote
Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique
QR Code

Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique

Bibliographic Details
Format: Restricted Document
  • Holdings
  • Description
  • Similar Items
  • Staff View

Similar Items

  • Multi-class Pattern Classification in Imbalanced Data
    by: Ghanem, Amal, et al.
    Published: (2010)
  • Synthetic Minority Over-Sampling Technique (SMOTE) and Logistic Model Tree (LMT)-adaptive boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles
    by: Amirruddin, Amiratul Diyana, et al.
    Published: (2022)
  • Logistic regression methods for classification of imbalanced data sets
    by: Santi Puteri Rahayu, -
    Published: (2012)
  • Improving sentiment analysis accuracy using CRNN on imbalanced data: a case study of Indonesian National Football coach
    by: Riyadi, Slamet, et al.
    Published: (2024)
  • Learning in imbalanced relational data
    by: Ghanem, Amal, et al.
    Published: (2008)

Search Options

  • Advanced Search

Find More

  • Browse the Catalog

Need Help?

  • Search Tips