TY  - JOUR
T1  - Comprehensive Benchmarking Analysis for Evaluating Effectiveness of Transfer Learning-based Feature Engineering in AutoML
AU  - Sırt, Merve
AU  - Eyüpoğlu, Can
PY  - 2025
DA  - February
Y2  - 2024
DO  - 10.52876/jcs.1604889
JF  - The Journal of Cognitive Systems
JO  - JCS
PB  - İstanbul Technical University
WT  - DergiPark
SN  - 2548-0650
SP  - 30
EP  - 37
VL  - 9
IS  - 2
LA  - en
AB  - This study conducts a comprehensive benchmarking analysis to evaluate the effectiveness of transfer learning-based feature engineering in Automated Machine Learning (AutoML) systems. The research compares traditional manual feature engineering, standard AutoML approaches, and transfer learning-enhanced AutoML across diverse data modalities, including images, text, and tabular data. Experimental evaluations were carried out using CIFAR-10, IMDB Reviews, and Adult Census Income datasets, focusing on assessing each approach in terms of model performance, training time, and resource utilization. The findings reveal that transfer learning-enhanced AutoML significantly reduces training time by up to 45% while improving model accuracy by up to 20%, particularly for image and text datasets. Furthermore, scenarios with high feature reuse rates demonstrated memory utilization improvements of up to 30%. These results underscore the substantial advantages of integrating transfer learning into AutoML systems for optimizing feature engineering processes.
KW  - AutoML
KW  - Transfer Learning
KW  - Feature Engineering
KW  - Machine Learning Optimization
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UR  - https://doi.org/10.52876/jcs.1604889
L1  - https://dergipark.org.tr/en/download/article-file/4456103
ER  -