Streaming Active Learning for Regression Problems Using Regression via
Classification
- URL: http://arxiv.org/abs/2309.01013v2
- Date: Fri, 15 Dec 2023 16:01:41 GMT
- Title: Streaming Active Learning for Regression Problems Using Regression via
Classification
- Authors: Shota Horiguchi, Kota Dohi, Yohei Kawaguchi
- Abstract summary: 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 can be applied directly to regression problems.
- Score: 12.572218568705376
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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