Streaming Active Learning for Regression Problems Using Regression via Classification
This work addresses the problem of model degradation in industrial regression tasks, offering an incremental adaptation of existing classification methods to a less-explored area.
The paper tackles the challenge of maintaining model performance in changing environments by proposing a streaming active learning method for regression problems, using regression-via-classification to apply classification-based techniques, achieving higher accuracy at the same annotation cost in experiments on four real datasets.
One of the challenges in deploying a machine learning model is that the model's performance degrades as the operating environment changes. To maintain the performance, streaming active learning is used, in which the model is retrained by adding a newly annotated sample to the training dataset if the prediction of the sample is not certain enough. Although many streaming active learning methods have been proposed for classification, few efforts have been made for regression problems, which are often handled in the industrial field. In this paper, we propose to use the regression-via-classification framework for streaming active learning for regression. Regression-via-classification transforms regression problems into classification problems so that streaming active learning methods proposed for classification problems can be applied directly to regression problems. Experimental validation on four real data sets shows that the proposed method can perform regression with higher accuracy at the same annotation cost.