QLS Seminar - Tue. 16 July at 11:00am - "A Hybrid Data Partitioning for Enhanced Machine Learning in High-Cost Systems" by Christopher Udomboso (QLS Associate)

[Quantitative Life Sciences]

Dear All,
Tomorrow,  Tuesday, 16 July at 11:00 CET, Christopher Udomboso 
(University of Ibadan, NIgeria) will give a seminar titled:

*A Hybrid Data Partitioning for Enhanced Machine Learning in High-Cost 
Systems
*

_Abstract:_

Effective data partitioning is critical in machine learning, especially 
in high-cost physical systems. Cross- validation techniques like K-Fold 
Cross-Validation (KFCV), though used to enhance model robustness, is 
known to compromise generalization assessment due to extensive 
computation and data shuffling demands. Simple Random Sampling (SRS) is 
used in this study to mitigate the weaknesses of KFCV. While SRS ensured 
representative samples, it risked non-representative sets in imbalanced 
data. Integrating both methods minimized biases, blending the simplicity 
of the SRS with the accuracy of the KFCV to optimize data partitioning. 
The hybrid method enhanced mean and variance convergence across diverse 
dataset sizes and trials, effectively balancing performance under 
various conditions. It proved beneficial in resource- constrained 
environments and with extensive datasets, providing practical solutions 
for effective machine learning implementations.
_
The seminar will take place in the *Common area*, Ex SISSA building, 
Second floor, via Beirut 2_
__
You are all most welcome to attend!

Best regards,
Erica

-- 
Erica Sarnataro
Group Secretary
Quantitative Life Sciences
The Abdus Salam International Centre for Theoretical Physics (ICTP)
Trieste,  Italy
Tel. +39-040-2240623
www.ictp.it/research/qls.aspx
e-mail:qls at ictp.it