Streaming Active Learning for Regression Problems Using Regression via Classification

TL;DR

This paper introduces a novel streaming active learning method for regression that transforms regression tasks into classification problems, enabling the use of classification-based active learning techniques for improved accuracy in industrial applications.

Contribution

It proposes a regression-via-classification framework for streaming active learning in regression, filling a gap in existing methods primarily focused on classification.

Findings

Higher regression accuracy at the same annotation cost

Effective application of classification-based active learning to regression

Validated on four real-world datasets

Abstract

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.