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Data Augmentation Techniques for Improving Machine Learning Model Accuracy
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
Data augmentation has emerged as a critical technique in machine learning, enhancing model accuracy by artificially expanding training datasets. By applying transformations and synthetic data generation methods, data augmentation improves generalization, mitigates overfitting, and strengthens model robustness, especially in scenarios where data collection is limited or expensive. This explores various data augmentation techniques across different data types, including images, text, audio, tabular, and time-series data. In image processing, data augmentation techniques such as rotation, flipping, scaling, and adversarial perturbations enhance the diversity of visual datasets. For natural language processing (NLP), synonym replacement, back translation, and large language model-based augmentation improve textual data variability. Audio and speech data benefit from techniques like time-stretching, pitch shifting, and background noise injection, which help models adapt to real-world environments. In tabular and time-series data, methods such as SMOTE, jittering, and synthetic sequence generation contribute to balancing datasets and capturing temporal patterns effectively. Despite its advantages, data augmentation poses challenges, including potential loss of data integrity, computational costs, and the risk of introducing biases. Ensuring that augmented data maintains meaningful relationships within the dataset is crucial to preventing model degradation. Additionally, the computational overhead of generating high-quality synthetic data remains a constraint in large-scale applications. Future advancements in AI-driven data augmentation, including self-supervised learning and reinforcement learning-based augmentation, are expected to revolutionize data preprocessing. Automated augmentation pipelines and domain-specific strategies will further refine model performance across diverse industries, from healthcare to finance. By leveraging innovative augmentation techniques, researchers and practitioners can develop more accurate, robust, and generalizable machine learning models. This paper provides a comprehensive analysis of the role, methodologies, challenges, and future trends in data augmentation, highlighting its significance in modern machine learning workflows.
Keywords
Data augmentation, Techniques, Machine learning, Model accuracy
References
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How to cite this paper
@article{1708013,
author = {Unomah Success Ugbaja, Uloma Stella Nwabekee, Wilfred Oseremen Owobu, Olumese Anthony Abieba},
title = {Data Augmentation Techniques for Improving Machine Learning Model Accuracy},
journal = {Iconic Research And Engineering Journals},
year = {2022},
volume = {5},
number = {10},
pages = {354-364},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708013.pdf},
abstract = {Data augmentation has emerged as a critical technique in machine learning, enhancing model accuracy by artificially expanding training datasets. By applying transformations and synthetic data generation methods, data augmentation improves generalization, mitigates overfitting, and strengthens model robustness, especially in scenarios where data collection is limited or expensive. This explores various data augmentation techniques across different data types, including images, text, audio, tabular, and time-series data. In image processing, data augmentation techniques such as rotation, flipping, scaling, and adversarial perturbations enhance the diversity of visual datasets. For natural language processing (NLP), synonym replacement, back translation, and large language model-based augmentation improve textual data variability. Audio and speech data benefit from techniques like time-stretching, pitch shifting, and background noise injection, which help models adapt to real-world environments. In tabular and time-series data, methods such as SMOTE, jittering, and synthetic sequence generation contribute to balancing datasets and capturing temporal patterns effectively. Despite its advantages, data augmentation poses challenges, including potential loss of data integrity, computational costs, and the risk of introducing biases. Ensuring that augmented data maintains meaningful relationships within the dataset is crucial to preventing model degradation. Additionally, the computational overhead of generating high-quality synthetic data remains a constraint in large-scale applications. Future advancements in AI-driven data augmentation, including self-supervised learning and reinforcement learning-based augmentation, are expected to revolutionize data preprocessing. Automated augmentation pipelines and domain-specific strategies will further refine model performance across diverse industries, from healthcare to finance. By leveraging innovative augmentation techniques, researchers and practitioners can develop more accurate, robust, and generalizable machine learning models. This paper provides a comprehensive analysis of the role, methodologies, challenges, and future trends in data augmentation, highlighting its significance in modern machine learning workflows.},
keywords = {Data augmentation, Techniques, Machine learning, Model accuracy},
month = {April},
}