An Online, Adaptive and Unsupervised Regression Framework with Drift
  Detection for Label Scarcity Contexts
Rene Richard Nabil Belacel
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. Finally, 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.