Adaptive Stochastic Gradient Descent (SGD) for erratic datasets
Küçük Resim Yok
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Stochastic Gradient Descent (SGD) is a highly efficient optimization algorithm, particularly well suited for large datasets due to its incremental parameter updates. In this study, we apply SGD to a simple linear classifier using logistic regression, a widely used method for binary classification tasks. Unlike traditional batch Gradient Descent (GD), which processes the entire dataset simultaneously, SGD offers enhanced scalability and performance for streaming and large-scale data. Our experiments reveal that SGD outperforms GD across multiple performance metrics, achieving 45.83% accuracy compared to GD's 41.67 %, and excelling in precision (60 % vs. 45.45 %), recall (100 % vs. 60 %), and F1-score (100 % vs. 62 %). Additionally, SGD achieves 99.99 % of Principal Component Analysis (PCA) accuracy, slightly surpassing GD's 99.92 %. These results highlight SGD's superior efficiency and flexibility for large-scale data environments, driven by its ability to balance precision and recall effectively. To further enhance SGD's robustness, the proposed method incorporates adaptive learning rates, momentum, and logistic regression, addressing traditional GD drawbacks. These modifications improve the algorithm's stability, convergence behavior, and applicability to complex, large-scale optimization tasks where standard GD often struggles, making SGD a highly effective solution for challenging data-driven scenarios.
Açıklama
Anahtar Kelimeler
Gradient descent, Stochastic Gradient Descent, Accuracy, Principal Component Analysis
Kaynak
Future Generation Computer Systems-The International Journal of Escience
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
166
