A REVIEW OF ROBUST ONLINE LEARNING MODELS IN HIGH-NOISE SCENARIOUS: MACHINE LEARNING APPROACHES TO N by IRJET Journal

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 inflowing streams of data are experiencing a great challengeintermsofrobustness,whenappliedtothehighly 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,limitedmemoryspace,continuousadaptation.

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 realtimehealthcareanalytics fromwearabledevices.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,andincorporateXAItoreviewimpactsofnoise. Filling the gaps between machine learning, signal processing,anddomain-specificcompetence willbea keyto the online systems that can flourish in the unpredictable, noisyenvironments.

Key Words: NoiseReductioninMachineLearning,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 reliabilityofmodelsduringtheirtrainingordeploymentin 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

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, andonline-specifictools.

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)andactivelearningtoverifylabelsallowsrealtime learning without compromising computational efficiency.

Metrics for measuring noise reduction efficacy include such metrics as accuracy under noise, recovery time after conceptdrift,andfalsepositiverates.Effortsarestillfaced inscalabilityofdatainhighdimensions(forinstancevideo 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.

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1.1 Motivation

The increasing dependence upon real-time machine learning applications-starting from the IoT-enabled predictive maintenance to the algorithmic trading and autonomous robotics-spells out the sense of urgency for noise-resilientmodels.Theyworkincircumstanceswhere the delay or the inaccuracy of decision-making can cause catastrophic failures. For example, in the field of healthcare, noisy data in wearables may hamper the realtimepatientsurveillance,andinthefinanceworld,volatile signals from the market environment may undermine the well-functioning of high-frequency trading algorithms. Costsofunreliablemodelsintheseareasdemonstratethe significance of building reliable online learning systems which are still accurate even if they receive noisy inputs. Moreover, the growth of edge computing and decentralized learning paradigms increases the need for lightweight and adaptive algorithms that provide a good balance between the efficiency in computations and the toleranceofnoises.

1.2 Problem Statement

Onlinemodelsforlearningaremoreandmorebeingused intherealworldsituationswherethestreamsofdata are intrinsically noisy, given the sensor errors, mislabeled samples, adversarial attacks, and concept drift. This noise adversely affects the capability of the model by injecting corrupted features, spurious labels, and outliers, and this resultsinoverfitting,unreliableforecasts,andforgetting –maintain (loss of previously learned information). These obstacles are further enhanced by the linear nature of online learning since models seek to change gradually without going back over previous input data thus hard to correctmistakes.Suchfailuresindomainsthataresafetycritical such as; autonomous systems, and healthcare can have catastrophic operational, financial, or ethical consequences. Furthermore, cooperation of nonstationary data distribution as well as resource requires (e.g., limited memory and real-time processing needs) further exacerbates the design of robust online learning structures.

1.3 Objectives

This review paper aims to: (1) systematically analyze state-of-the-art noise reduction strategies tailored for online learning, including data preprocessing techniques (e.g., adaptive filtering), model-centric approaches (e.g., robustlossfunctions),andhybridframeworks(e.g.,metalearning with drift detection); (2) evaluate the trade-offs between robustness, computational efficiency, and accuracy inherent to these strategies, particularly in resource-constrainedsettings;and(3)identifyunresolved challenges, such as scalability in high-dimensional data streams, adversarial noise mitigation, and ethical implicationsofbiasamplification.Bysynthesizinginsights from existing research, this work proposes future directionsforinterdisciplinarycollaboration,emphasizing theintegrationofmachinelearning,signalprocessing,and domain-specific expertise to advance noise-agnostic onlinelearningsystems.

2. BACKGROUND

2.1 Types of Noise in Data

In machine learning datasets, noise assumes various shapes and has its own way of affecting model performance differently.Label noise istheinaccuraciesin training labels, relating to a misclassified image in a dataset, caused by human biases or subjective interpretation of human annotations. In turn, the feature noise is the corruption of the input variables, including those in the IoT devices in which sensors make errors on temperature readings. Concept drift refers to temporal changes in data distribution on distributions over time, includinganalterationinconsumertastesine-commerce, whichmade history irrelevant. Adversarial noiseisa type

Figure-1: Noise Prediction Using Machine Learning with Measurements Analysis

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of noise that includes deliberate perturbations meant to mislead models which include manipulated inputs that make autonomous vehicles misclassify the traffic signs. Thesources of thesenoise typesare diverse, for example, hardware limitation (e.g., faulty sensors in robotics), human error in manual labelling (e.g., ambiguous medical diagnosis), and attack on model weaknesses (e.g., adversarialexampleinspamdetectionsystem).

2.2 Online Learning Fundamentals

Online learning has a different nature to traditional batch learning, which operates in a sequential fashion and incrementally updates models. Whereas batch learning learns from static datasets by several passes, online learninglearnsdynamicallyfrompoint-wisedataandthus fits changing environments such as financial markets or IoT networks. However, such an approach involves such difficulties as real-time processing constraints, where the modelsneedtobeupdatedinreal-timetopreventlatency. limited memory, that makes it impossible to store large volumes of historical data for retraining; and nonstationary data, where concept drift and the changing nature of noise continuously require adaptations. These challenges require lightweight, adaptive algorithms that can strike an equilibrium between stability and adaptabilitytoperforminuncertainenvironments.

Figure-2: CNN-based noise reduction for multi-channel speech enhancement system with discrete wavelet transform (DWT)

2.3 Impact of Noise on Model Performance

Noise has huge implications to the effectiveness of machine learning models especially in the online context. Accuracy degradation happens when the corrupted features or samples that are wrongfully labelled produce wrong predictions – an example is false alerts for fraudulent activities in financial setups. It occurs when models memorize the noise rather than learn general patterns,thereforedegradingrobustnessonoutofsample data.Catastrophicforgettingisoneofthemajorproblems encountered during incremental learning, and it occurs when the models rapidly adapt to noisy streams and forgetwhathaspreviouslybeenlearned,e.g.,autonomous

vehicles that do not recognize rare road scenarios. Realworld examples highlight these risks: in healthcare, the noisy data from wearable devices can activate false alerts for the patients, while adversarial patches on the road signs made the autonomous cars misread traffic signals. Withsuchfailures,thereisurgentneedfornoise-resilient modelsinsafety-criticalapplications.

3. TAXONOMY OF NOISE REDUCTION STRATEGIES

3.1 Data Preprocessing Techniques

Data preprocessing efforts involve cleaning the noisy raw databeforefeedingittothemodel.Temporaldatastream smoothing filtering techniques like moving averages or Kalman filters denoice high frequencies, whereas wavelet transforms attenuate frequency domains to isolate and suppress noise. Outlier detection mechanisms such as online clustering algorithms like streaming k-means, or statistical threshold detect anomalous data points, and remove them in real time. Data augmentation makes up for noise by producing synthetic instances that resemble the clean data distributions, like perturbing elements in the safe range to improve generalization of the models. These are lightweight computationally, what is desireable for online learning’s need of real-time processing, but if noisethresholdsaretooaggressive,they mayaccidentally tossoutmeaningfulpatterns.

3.2 Model-Based Strategies

Model-basedapproachesincreaserobustnesstonoiseasa result of novel architectural or algorithmic design. Sturdy lossfunctions,likeHuberlossorbootstraploss,weighthe effect of noisy samples less when training is performed, hence not affecting the model significantly. Ensemble methods such as online boosting or dynamic classifier selection combine predictions from a weak plurality to blur the impact of noise by drawing on diversity for collectiverobustness.Insuchtechniquesofregularization such as adaptive dropout or deliberate noise injection during training, overfitting is avoided by making the models learn noise-invariant features. Although those approaches enhance generalization, many of them necessitate fine-tuning between robustness to the computational burden, especially in resource-limited onlineenvironments.

3.3 Hybrid Approaches

Hybrid approaches combine techniques of data-centric and model-centric to handle noise in a dynamic manner. Noise-aware architectures, including recurrent neural networks with gated mechanisms (e.g., LSTM, GRU), have implicitnoisesuppressionabilitybecausetheyretainonly relevant temporal patterns, and attention mechanisms that are more reliable in input sequences. Meta-learning frameworks approach the problem from a “learning-to-

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learn” perspective and the models dynamically adjust their noise handling strategies, using the information about observed data properties. For example, metaoptimizerscan,dependingonthedetectedconceptdriftor adversarial perturbations, adjust loss functions or sampling policies. These approaches are best at complex, non-stationary environments, but they are usually computationally expensive, which does not make them usableinlow-latencysettings.

3.4 Online-Specific Strategies

Online-specificstrategiesarespecifictothepeculiaritiesof the streaming data. The incremental learning with noise resilience utilizes the adaptive windowing procedure to throw away outdated or noisy data segments along with the distribution shifts detection algorithms such as ADWIN or DDM to initiate model retuning when the distribution shifts occur. Active learning reduces the negative effect of label noise because it queries uncertain or impactful samples to human annotators in selective ways e.g. query-by-committee or uncertainty sampling. Feedback loops incorporate human-in-the-loop system to correct model errors on a real-time basis like validating predictioninasafety-criticalhealthcaremonitoring.These strategies focus on adaptability and efficiency, but they only work if there is timely feedback and a high-quality human-machineinteraction.

4. EVALUATION METRICS AND METHODOLOGIES

4.1 Benchmarking Noise Resilience

Computingtherobustnessofonlinemodelsoflearningfor noisyenvironmentsrequiresmetricscapabletoreflectthe performance stability and capability of adaptation. Under noise, model accuracy denotes the model’s ability to remain predictorily accurate even with corrupted inputs, whereas recovery time after concept drift measures how rapidlyamodelwouldre-calibratetonewdistributionsof data post-disturbance. False positive/negative rates are essential in such safety-critical areas as healthcare or fraud identification where the misclassifications can lead to substantial costs. For instance, a watchful model over ICU patient data needs to reduce the false negatives in order not to miss life-threatening aberrations. These metrics together measure how resilient a model is, althoughincontexttheymustbeinterpreted,asthereare alsooftentrade-offsbetweenspeedandprecision.

4.2 Datasets and Simulated Noise

The benchmarking of noise reduction strategies needs to use datasets that mimic real-world imperfections. Common datasets such as MNIST or CIFAR-10 are frequently corrupted with added noise, such as Gaussian perturbation, label shuffling, or adversarial patches in order to generate noise conditions of a certain scope. However,syntheticnoisemightnotreflectcomplexitiesof real-world data streams the way it should. More true to life noisy datasets, like sensor output from IoT deployments or social media flows with built-in label contradictions, make for proper testing fields. For example, the UCI Human Activity Recognition (HAR) dataset, collected from smartphone accelerometers, has natural sensor noise in it and is thus perfect for testing real-time filtering techniques. Synthetic and real-world datasets when used would makea combination providing forthefullevaluationofnoise-handlingskills.

4.3 Experimental Frameworks

Experimentation frameworks need to be robust such that reproducible comparisons of noise reduction strategies can be made. Such frameworks as TensorFlow and PyTorch allow for elastic implementation of online learningalgorithmswithlibrariescateringforcustomloss functions,non-staticarchitectures,andlivedatapipelines. Specialized tools such as MOA (Massive Online Analysis) includethenativesupportforstreamingdataandconcept drift detection, incremental evaluation so that conducting experiments on high-velocity datasets is simplified. For instance, MOA offers adaptive windowing and drift detection module through which researchers can test algorithms under simulated non-stationary conditions. Open-sourcereproducibility,withthehelpofsuchtoolsas Weka or Scikit-multiflow, provides for transparency, though there are also still difficulties to standardize evaluation protocols for all the types of noise and applicationdomains.

5. CHALLENGES AND OPEN PROBLEMS

5.1 Trade-offs in Real-Time Systems

The linkingofcomputational costand the effectiveness of noisereductionsstrategiestoonlinelearningisoneofthe major challenges in implementing noise reduction strategies to online learning. Lightweight algorithms are required for real-time systems (autonomous vehicles or edge-computing IoT) to be consistent with the latencyconstrainedrequirements.However,richnoise-processing tricks – that is, meta-learning frameworks or attention mechanisms – frequently demand heavy computational parameters, and there is tension between robustness and efficiency. For example, while the ensemble techniques enhance robustness due to the aggregation of several models, the ramifications in terms of memory and time

Figure-3: Structure and components of noise models.

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might be too high for resources-limited edge platforms. Maintaining this balance is essential in the safety-critical applications, because delayed or computationally expensivecalculationscancausetheoperativefailures.

5.2

Scalability

Its scalability is still a constant barrier especially in the processingofhigh-dimensionaldatastreamssuchasvideo feeds, multi-sensor IoT networks or genomic sequences. Conventional noise reduction approaches, like statistical filtering or clustering, have unsuccessful battles with the curseof dimensionality – whencomputational complexity escalates with a drastic rate with increasing features. For instance, real-time denoising of high-resolution video streams needs algorithms processing thousands of pixels per frame without increasing latency. The traditional approaches frequently use dimensionality reduction or use approximations, but a loss of potentially important patterns is a danger of these approximations. Designing scalable algorithms that maintain fidelity through processing gigantic amounts of data in a rapid tandem is stillanopenresearchhorizon.

5.3

Adversarial Noise

As malicious entities gain model exploits in very carefully designed perturbations within the online setting, the threat that brings forth adversarial attacks is unique. Unlike the random noise, adversarial noise is formulated to escape detection, for example, small changes in pixels along camera feeds to confuse autonomous vehicles. Current defense mechanisms such as the adversarial training or gradient masking are computationally costly and cannot cope with real-time stream environments. In addition, adversarial attacks are constantly changing, necessitating the constant updating of the model that contradicts the need for stability of online learning. In order to protect the systems from these targeted threats, designing lightweight, proactive defenses, namely, realtime anomaly detection or certified robustness frameworks,iscritical.

5.4 Generalization Across Domains

Cross-application portability of noise resilience strategies is still a great challenge; owing to the heterogeneity of noise profiles and unique data characteristics in domains. Solutions which work in one scenario, for example, Kalman filtering for IoT sensor data, do not necessarily succeedelsewhere,forexample,socialmediatextstreams contaminated by linguistic noise. For instance, label correction methods developed for medical images data may not apply to financial time-series data, where noise patterns are stochastic and non-stationary. It takes such frameworks that learn noise-agnostic representations or dynamically restructure strategies according to domain metadata in order to bridge this gap. Besides, ethical

issues, like amplification of bias when moving models from one socio-cultural context to another, complicate cross-domain generalization, and an interdisciplinary solutionisneeded.

6. CASE STUDIES AND APPLICATIONS

6.1 IoT and Edge Computing

Noise reduction is important in the IoT and edge computing context to make predictive maintenance plausible in an industrial and environmental monitoring context. The sensor data from the machinery or environmental sensor usually has noise arising from hardware failure, electromagnetic interference or severe operation environment. For instance, vibration sensors in manufacturing processors might cause spurious signal because of physical wear thus causing false alarm or failure predictions. It uses techniques such as adaptive Kalman filters or online clustering algorithms to cleanse sensor streams in real time, so as to differentiate true anomalies from noise. By adopting these strategies, systems could achieve higher fault detection accuracy, longer lifespan of the equipment and reduced unplanned downtime.Nevertheless,therearestillissuesinachieving the balance between computational efficiency and noise suppression and, specifically, for low-power edge devices thathandlehigh-frequencysensormeasurements.

6.2 Financial Markets

High-frequency trading (HFT) systems are based on the processingofnoisyfinancialsignalsinrealtime,whenthe marketvolatility,newssentiment,andalgorithmictrading addunpredictable noise.For example,speculative trading or incorrect data feeds may trigger unexpected price fluctuationswhichmayleadtopoortradesbeingmadeby trading algorithms. Strong online models like ensemble techniqueswithdynamicselectionoftheclassifierordrift detection algorithms such as ADWIN assist in screening transient noise without losing important market trends. These models have a focus on low-latency processing in order to remain competitive, but should avoid overfitting to short-term noise that may upset long-term portfolios. Practical deployments show decline in slippage, trade execution accuracy, but scalability to global markets with non-uniformnoiseprofilesisanopenchallenge.

6.3 Healthcare Monitoring

The use of wearable devices and remote patient monitoring systems gives rise to a sequence of continual health data streams that often get polluted with motion artifacts, sensor shifting, or physiological signal irregularities. For instance, smartwatch ECG readings maybe polluted by activities like walking which results intofalselyidentifiedarrhythmias.Solving the problem of noiseeliminationbysuchmeansaswavelettransformsfor

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signal denoising or hybrids of RNNs with attention mechanisms can isolate clinically meaningful patterns. Label noise is further reduced through active learning mechanisms with a real-time querying of clinicians to confirmquestionablepredictions.Thesemethodsincrease the trustworthiness of the real-time analytics, so early diagnoses of such conditions as atrial fibrillation or hypoglycemia are possible. However, ethical concerns about patient’s privacy and algorithmic bias require a carefulimplementationwhileintegratingapplicationsinto healthcaretoguaranteetrustandsafety.

7. FUTURE DIRECTIONS

7.1 Adaptive Noise Thresholding

For ahead developments and growth in noise-resilient online learning, the priority will be posited on dynamic adjustabilityoflevelsofnoisethresholdsusingself-tuning models and on-the-spot data parameters. These models mayusereinforcementlearningorBayesianparadigmsto independently adjust the sensitivity to noise severity, for instance,becomingmoreaggressiveinfilteringinthecase of a malfunction of sensors in IoT systems or loosening label correction during the stable data periods. For example,apredictivemaintenancemodelintheindustrial IoTmaydecreaseoutlierdetectionlimitswhenstartingup machinery (a high-vibration phase) so as to prevent false alarms. Challenges however will be the balance between adaptability and stability in order to avoid oscillatory behavior, and computational efficiency for edge deployment.

7.2 Integration with Explainable AI (XAI)

With the increase in the complexity of noise reduction methods, incorporating explainable AI (XAI) becomes essential to understand how noise influences choices of a model. Methods such as attention maps or saliency analysis could indicate whether models prefer noisy or clean traits to let developers optimize preprocessing pipelines or structural decisions. For instance, in health care, the reason for why a patient monitoring system misclassified a noisy ECG signal might help in modifying sensor placement or fine-tuning the algorithm. But XAI methods should be robust to noise themselves –explanations computed from corrupted data may mislead the stakeholders. Filling the gap between interpretability and noise resistance increases trust and helps with compliancewithregulationsinsensitivedomains.

7.3 Federated Learning for Noisy Environments

Federated learning (FL) provides a promising approach for a decentralized noise reduction while providing joint model training between distributed devices while avoiding centralization of raw data. In FL frameworks, edge devices may locally denoise data (e.g., smartphones

filtering motion artifacts from wearable sensors) prior to communicating model update to another edge/cloud devices. Nevertheless, disparities in the distributions of noise in devices like an urban vs. rural environmental sensor data might ruin global model performance. Ideas such as personalized noise profiles or secure aggregation protocols weighing contributions by the local noise severity might solve this issue. For instance, an ECG monitoring system that is federated could give weight to cleaner signal histories and flag noisy participants for recalibration.

7.4 Ethical Considerations

Noise being distributed in an uneven manner between demographicorgeographicalgroupsthreatenstomagnify machine learning system biases. For example, some populations may have noisier data from wearable health devices because of the skin tone or movement patterns, resulting in inequitable diagnostic accuracy. Similarly, there are adversarial attacks against specific groups (e.g. biased perturbations in facial recognition systems) that wouldaggravatediscrimination.Todealwiththeseissues, fairness-aware noise reduction methods like bias audits on preprocessing or adversarial debiasing in the training ofmodelsarenecessary.Regulatoryframeworkstoomust change to cover transparency in terms of noise handling practices and ensuring equal outcomes among different usergroups.

8. CONCLUSION

The explosion of real-time machine learning usage in dynamic, noisy settings highlights the imperative requirements of the noise reduction methodologies in online learning. The pieces of often found state-of-the-art techniques, i.e., data preprocessing, model-based adaptations, hybrid frameworks, and online-specific mechanisms, contributing to the model resilience to label noise, adversarial perturbations, and concept drift, are successfully synthesized in this review. Although these strategies showcase promising outcomes in areas such as IoT, finance, and health care, the problems remain in a compromise between computational efficacy and robustness, in scaling well to large-dimensional streams, and in generalization across heterogeneous domains. In the future, efforts have to focus on flexible models that self-adjust to the degree of noise, facilitate transparent decisions by using explainable AI, and utilize federated learning in coping with distributed noise issues. Ethical concerns, especially over bias amplification due to unbalanced levels of noise distributions, require immediate attention in providing an equitable conclusion. With the combination of machine learning and the signal processing, systems engineering, and domain expertise, theresearcherscandevelopnoise-agnosticonlinesystems that can survive in the disordered, noisy environment typicalformodernreal-worldapplications.

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