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A Conditioned Unsupervised Regression Framework Attuned to the Dynamic Nature of Data Streams

Rene Richard, Nabil Belacel

TL;DR

This work tackles regression in streaming contexts where real-time labels are scarce. It introduces an online, adaptive, unsupervised regression framework built on two models trained on independent data sources, coupled with sliding-window updates and a drift-detection mechanism that can incorporate ADWIN and RMSE-based criteria. The approach demonstrates improved predictive accuracy across multiple UCI datasets (Air Quality, Concrete, Protein, Turbine) at the cost of some extra computation due to retraining, and highlights trade-offs between responsiveness to drift and processing time. The results underscore the practicality of near real-time, label-efficient adaptation for evolving data distributions in industrial and scientific settings, while identifying avenues for dynamic thresholds and broader comparisons in future work.

Abstract

In scenarios where obtaining real-time labels proves challenging, conventional approaches may result in sub-optimal performance. This paper presents an optimal strategy for streaming contexts with limited labeled data, introducing an adaptive technique for unsupervised regression. The proposed method leverages a sparse set of initial labels and introduces an innovative drift detection mechanism to enable dynamic model adaptations in response to evolving patterns in the data. To enhance adaptability, we integrate the ADWIN (ADaptive WINdowing) algorithm with error generalization based on Root Mean Square Error (RMSE). ADWIN facilitates real-time drift detection, while RMSE provides a robust measure of model prediction accuracy. This combination enables our multivariate method to effectively navigate the challenges of streaming data, continuously adapting to changing patterns while maintaining a high level of predictive precision. We evaluate the performance of our multivariate method across various public datasets, comparing it to non-adapting baselines. Through comprehensive assessments, we demonstrate the superior efficacy of our adaptive regression technique for tasks where obtaining labels in real-time is a significant challenge. The results underscore the method's capacity to outperform traditional approaches and highlight its potential in scenarios characterized by label scarcity and evolving data patterns.

A Conditioned Unsupervised Regression Framework Attuned to the Dynamic Nature of Data Streams

TL;DR

This work tackles regression in streaming contexts where real-time labels are scarce. It introduces an online, adaptive, unsupervised regression framework built on two models trained on independent data sources, coupled with sliding-window updates and a drift-detection mechanism that can incorporate ADWIN and RMSE-based criteria. The approach demonstrates improved predictive accuracy across multiple UCI datasets (Air Quality, Concrete, Protein, Turbine) at the cost of some extra computation due to retraining, and highlights trade-offs between responsiveness to drift and processing time. The results underscore the practicality of near real-time, label-efficient adaptation for evolving data distributions in industrial and scientific settings, while identifying avenues for dynamic thresholds and broader comparisons in future work.

Abstract

In scenarios where obtaining real-time labels proves challenging, conventional approaches may result in sub-optimal performance. This paper presents an optimal strategy for streaming contexts with limited labeled data, introducing an adaptive technique for unsupervised regression. The proposed method leverages a sparse set of initial labels and introduces an innovative drift detection mechanism to enable dynamic model adaptations in response to evolving patterns in the data. To enhance adaptability, we integrate the ADWIN (ADaptive WINdowing) algorithm with error generalization based on Root Mean Square Error (RMSE). ADWIN facilitates real-time drift detection, while RMSE provides a robust measure of model prediction accuracy. This combination enables our multivariate method to effectively navigate the challenges of streaming data, continuously adapting to changing patterns while maintaining a high level of predictive precision. We evaluate the performance of our multivariate method across various public datasets, comparing it to non-adapting baselines. Through comprehensive assessments, we demonstrate the superior efficacy of our adaptive regression technique for tasks where obtaining labels in real-time is a significant challenge. The results underscore the method's capacity to outperform traditional approaches and highlight its potential in scenarios characterized by label scarcity and evolving data patterns.
Paper Structure (32 sections, 7 equations, 5 figures, 3 tables)

This paper contains 32 sections, 7 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Materials and Method
  4. Background and problem setting
  5. Ordinary least squares for linear regression
  6. Handling concept drift with ADWIN and the generalization error of regression models
  7. Online adaptive unsupervised regression framework
  8. Datasets for benchmarking
  9. Air quality
  10. Concrete
  11. Protein
  12. Turbine
  13. Experiments and computational environment
  14. Discussion of the Results
  15. Drift detector
  16. ...and 17 more sections

Figures (5)

  • Figure 1: Online Adaptive Unsupervised Regression Framework
  • Figure 2: Air Quality CO (GT) - Predicted and Ground Truth Values
  • Figure 3: Concrete Compressive Strength - Predicted and Ground Truth Values
  • Figure 4: Protein RMSD - Predicted and Ground Truth Values
  • Figure 5: Turbine TEY - Predicted and Ground Truth Values