Synthetic Data Generation and Evaluation Techniques for Classifiers in Data Starved Medical Applications

Synthetic Data Generation and Evaluation Techniques for Classifiers in Data Starved Medical Applications

With their ability to find solutions among complex relationships of variables, machine learning (ML) techniques are becoming more applicable to various fields, including health risk prediction. However, prediction models are sensitive to the size and distribution of the data they are trained on. ML...

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Bibliographic Details
Main Authors: Wan D. Bae, Shayma Alkobaisi, Matthew Horak, Siddheshwari Bankar, Sartaj Bhuvaji, Sungroul Kim, Choon-Sik Park
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Autoencoders
class imbalance problem
control coefficient
data starved contexts
rare event prediction
synthetic minority oversampling technique
Online Access:https://ieeexplore.ieee.org/document/10847858/
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