Guest Lecture on “Advanced Sampling Strategies for Imbalanced Learning”

Ramaiah University recently hosted a distinguished guest lecture by Dr. Asit Kumar Das, Professor at the Department of Computer Science and Technology, IIEST Shibpur. His talk, “From SMOTE to VAEs: Sampling Strategies for Imbalanced Learning”, explored the critical challenge of class imbalance in machine learning, particularly in domains such as disease detection, fraud analysis, and fault prediction.

Dr. Das highlighted the limitations of traditional models that often favor majority classes, leading to poor minority class recognition. He discussed classical resampling methods like SMOTE, Tomek Links, and hybrid approaches (SMOTE-ENN, SMOTE-Tomek), before moving to advanced deep learning techniques such as GANs and Variational Autoencoders (VAEs). Special emphasis was placed on VAEs, which provide stable training and probabilistic latent space modeling, making them effective for generating diverse synthetic samples in complex datasets like medical imaging.

The lecture also showcased experimental results on COVID-19 chest X-ray classification, demonstrating how latent space resampling with VAEs can improve recall and precision. Dr. Das concluded by stressing the importance of hybrid sampling and the potential of VAE-GAN combinations for future research.

This insightful session enriched students and faculty with cutting-edge strategies for tackling real-world imbalanced learning problems.