Year: 2025
Type: article
Abstract: Enhanced Self-Directed Training (ESDT) is a novel framework designed to enhance regression-based Multilayer Perceptron (MLP) models through strategic synthetic data augmentation. Unlike traditional methods that rely on random data generation, ESDT focuses on high-loss validation cases, applying Synthetic Data Extension (SDE) techniques, such as SMOTE-based interpolation for continuous data and time-based translation for discontinuous events. This strategy significantly improves model generalization, reduces overfitting, and boosts performance in unbalanced datasets. By tailoring data augmentation efforts to specific areas where the model encounters challenges, ESDT effectively increases data diversity and resilience, ensuring robust predictive modeling across various complex domains. This research establishes ESDT as a robust, adaptive training methodology, promising advancements in scalability and efficiency for future machine learning applications.
DOI: 10.13016/m2zez4-apuk
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7084601343
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