arxiv_ml 90% Match Research Paper Machine Learning Engineers,Data Scientists,Industrial Automation Specialists,Researchers in Online Learning 20 hours ago

An Adaptive Sampling Framework for Detecting Localized Concept Drift under Label Scarcity

ai-safety › robustness

📄 Abstract

Abstract: Concept drift and label scarcity are two critical challenges limiting the robustness of predictive models in dynamic industrial environments. Existing drift detection methods often assume global shifts and rely on dense supervision, making them ill-suited for regression tasks with local drifts and limited labels. This paper proposes an adaptive sampling framework that combines residual-based exploration and exploitation with EWMA monitoring to efficiently detect local concept drift under labeling budget constraints. Empirical results on synthetic benchmarks and a case study on electricity market demonstrate superior performance in label efficiency and drift detection accuracy.

Key Contributions

This paper introduces an adaptive sampling framework that efficiently detects localized concept drift under label scarcity. By combining residual-based exploration with EWMA monitoring, it overcomes the limitations of global drift detectors and dense supervision requirements, demonstrating superior label efficiency and accuracy.

Business Value

Enhances the reliability and robustness of predictive models in dynamic industrial environments where data distributions change and labeled data is scarce, leading to more accurate forecasts and better operational decisions.

Paper Metadata

Innovation Type

Algorithmic Framework

Deployment Feasibility

High. The framework is designed for dynamic environments and label scarcity, making it practical for real-world industrial applications.

Limitations Addressed

Existing drift detection methods assume global shifts and require dense supervision, making them unsuitable for local drifts and label-scarce regression tasks in dynamic industrial settings.

Performance Gains

Superior performance in label efficiency and drift detection accuracy compared to existing methods.

Technical Tags

concept drift detectionlabel scarcityadaptive samplinglocalized driftregression tasksEWMA monitoringresidual-based explorationdynamic environmentslabel efficiency

Research Topics

Machine LearningData MiningRobustnessOnline LearningIndustrial AI

Methods & Architectures

Adaptive SamplingResidual-based exploration/exploitationEWMA monitoringDrift detection algorithms

Applications & Tasks

Industrial Process Monitoring Financial Forecasting Real-time Analytics Predictive Maintenance Detecting localized concept driftHandling label scarcityRobust prediction in dynamic environments Concept drift detectionModel adaptationOnline prediction

Datasets & Benchmarks

Datasets

synthetic benchmarks, electricity market

Label efficiencyDrift detection accuracy

Related Fields

Machine LearningData MiningStatisticsIndustrial EngineeringTime Series Analysis

Keywords

concept driftlabel scarcityadaptive samplingonline learningrobustnessindustrial AIEWMAregressiondynamic environmentsmodel adaptation

Academic Context

#Machine Learning#Data Mining#Robustness#Online Learning#Industrial AI

Commercial Potential

Potential Products

Drift detection modules for MLOps platformsRobust forecasting systems for industrial applications

Target Industries

ManufacturingEnergyFinanceLogisticsRetail

Use Case Examples

Detecting changes in electricity market dynamics to improve price forecasting.Monitoring sensor data in manufacturing to detect subtle shifts affecting product quality.

Competitive Edge

Addresses the specific challenges of localized drift and label scarcity, offering a more practical solution than global drift detectors for many industrial scenarios.

Market Opportunity

Growing market for robust AI solutions in industrial automation and analytics.

Revenue Models

Software licensingconsulting services for implementation.

Resource Requirements

Compute Needs

Low to Moderate (designed for online/real-time processing)

Data Requirements

Streaming data, limited labeled data.

Deployment Constraints

Requires careful tuning of parameters (e.g., EWMA smoothing factor, sampling strategy).

Scalability

Designed for online processing, inherently scalable to high-volume data streams.

Production Readiness

Maturity Level

Research

Time to Market

1-3 years (integration into existing systems)

Patent Potential

Moderate (novel adaptive sampling framework)

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