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Journals / The Journal of Cognitive Systems / 2024 / Volume: 9 Issue: 2

Comprehensive Benchmarking Analysis for Evaluating Effectiveness of Transfer Learning-based Feature Engineering in AutoML

Merve Sırt(MİLLİ SAVUNMA ÜNİVERSİTESİ, ATATÜRK STRATEJİK ARAŞTIRMALAR ENSTİTÜSÜ)0009-0004-6348-2032
Can Eyüpoğlu(MİLLİ SAVUNMA ÜNİVERSİTESİ, ATATÜRK STRATEJİK ARAŞTIRMALAR ENSTİTÜSÜ)0000-0002-6133-8617
Pages
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Publication Date
2025-02-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study conducts a comprehensive benchmarking analysis to evaluate the effectiveness of transfer learning-based feature engineering in Automated Machine Learning (AutoML) systems.
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Key Findings & 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.
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Conclusion & Impact: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.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:AutoML · Transfer Learning · Feature Engineering

Abstract

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.

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Cite This Article

Sırt, M., Eyüpoğlu, C. (2025). Comprehensive Benchmarking Analysis for Evaluating Effectiveness of Transfer Learning-based Feature Engineering in AutoML. The Journal of Cognitive Systems, Volume: 9 Issue: 2. https://doi.org/10.52876/jcs.1604889

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