International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
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A REVIEW OF ROBUST ONLINE LEARNING MODELS IN HIGH-NOISE SCENARIOUS: MACHINE LEARNING APPROACHES TO NOISE REDUCTION Namarata Kumari1, Deepshikha2 1Master of Technology, Computer Science and Engineering, Lucknow Institute of Technology, Lucknow, India 2Assistant Professor, Department of Computer Science and Engineering, Lucknow Institute of Technology,
Lucknow, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The online learning models that adapt to
settings as IoT, healthcare, and finance, where the data streams are intrinsically non-stationary and noisy, robust noise-handling strategies come in handy. Such strategies are divided into four paradigms in general. data preprocessing, model-based practices, the hybrid design, and online-specific tools.
inflowing streams of data are experiencing a great challenge in terms of robustness, when applied to the highly noisy real-world data. Noise (due to mis-labeled samples, sensor errors, adversarial attacks, or concept drift) can severely degrade performance of the model causing overfitting, unreliable prediction, and catastrophic forgetting. Such problems are especially sharp in dynamic settings such as IoT, finance, and autonomous systems, where the data is non-stationary and the reinforcing feedback is weak. Classic learning of the batch pattern, formulated for the static dataset, is often unsuitable for the peculiarities of online environments, including real-time processing, limited memory space, continuous adaptation.
Data preprocessing techniques whittled input streams by smoothing temporal noise or outliers prior to training, such as the Kalman filters and adaptive outlier detection. Model-wise strategies improve intrinsic noise tolerance via robust loss functions (e.g., Huber loss), ensemble approaches (e.g., online boosting), and regularization approaches (e.g., noise-injection), which avoid the overfitting. Hybrid methods combine architectural advancements, such as using attention mechanisms in RNNs or meta-learning frameworks, to dynamically emphasizes reliable patterns, and adapt to changing patterns of noise. For online learning, special techniques such as incremental drift detection (for example, ADWIN algorithm) and active learning to verify labels allows realtime learning without compromising computational efficiency.
The implications of strong online learning spread from crucial areas. noise-resilient models can drive predictive maintenance in the Internet of Things networks, stabilize high-frequency trading algorithms, and advance the realtime healthcare analytics from wearable devices. Scalability in high-dimensional data streams, adversarial noise mitigation, and ethical issues such as bias amplification is critical to future research. Some of the evolving trends involve self-tuning models with the dynamic noise thresholds, federated learning for decentralized noise cancelation, and incorporate XAI to review impacts of noise. Filling the gaps between machine learning, signal processing, and domain-specific competence will be a key to the online systems that can flourish in the unpredictable, noisy environments.
Metrics for measuring noise reduction efficacy include such metrics as accuracy under noise, recovery time after concept drift, and false positive rates. Efforts are still faced in scalability of data in high dimensions (for instance video streams), defense against adversarial attacks, and maintaining generalization across domains. Future directions are for adaptive thresholding to self-tuned models, federated learning for controlling noise decentralized settings, and inclusion of explainable AI (XAI) to audit for noise impacts. Applications run from predictive maintenance in IoT, high-frequency trading, and wearable health analytics to emphasize the requirement for noise-agnostic systems in society. Researchers intend to create resilient frameworks, which can survive in the uncertain, noisy conditions of the artificial intelligence period in the future by bridging machine learning with signal processing, and domain expertise.
Key Words: Noise Reduction in Machine Learning, Online Learning Models, Robust Machine Learning, Noisy Data Streams, Concept Drift Adaptation, Real-Time ML Applications.
1. INTRODUCTION Noise reduction in machine learning addresses one of the most crucial challenges of preserving accuracy and reliability of models during their training or deployment in the imperfect, real-world data. Noise can take such forms as mislabeled samples, corrupted features, adversarial perturbations, or concept drift; these will lead to model performance degradation by introducing biases, overfitting, or catastrophic forgetting. In such dynamic
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