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.

Related papers

• Robust Capped lp-Norm Support Vector Ordinal Regression [85.84718111830752]
  Ordinal regression is a specialized supervised problem where the labels show an inherent order. Support Vector Ordinal Regression, as an outstanding ordinal regression model, is widely used in many ordinal regression tasks. We introduce a new model, Capped $ell_p$-Norm Support Vector Ordinal Regression(CSVOR), that is robust to outliers.
  arXiv  Detail & Related papers  (2024-04-25T13:56:05Z)
• Rethinking Classifier Re-Training in Long-Tailed Recognition: A Simple Logits Retargeting Approach [102.0769560460338]
  We develop a simple logits approach (LORT) without the requirement of prior knowledge of the number of samples per class. Our method achieves state-of-the-art performance on various imbalanced datasets, including CIFAR100-LT, ImageNet-LT, and iNaturalist 2018.
  arXiv  Detail & Related papers  (2024-03-01T03:27:08Z)
• Enhancing Consistency and Mitigating Bias: A Data Replay Approach for Incremental Learning [100.7407460674153]
  Deep learning systems are prone to catastrophic forgetting when learning from a sequence of tasks. To mitigate the problem, a line of methods propose to replay the data of experienced tasks when learning new tasks. However, it is not expected in practice considering the memory constraint or data privacy issue. As a replacement, data-free data replay methods are proposed by inverting samples from the classification model.
  arXiv  Detail & Related papers  (2024-01-12T12:51:12Z)
• Engression: Extrapolation through the Lens of Distributional Regression [2.519266955671697]
  We propose a neural network-based distributional regression methodology called engression' An engression model is generative in the sense that we can sample from the fitted conditional distribution and is also suitable for high-dimensional outcomes. We show that engression can successfully perform extrapolation under some assumptions such as monotonicity, whereas traditional regression approaches such as least-squares or quantile regression fall short under the same assumptions.
  arXiv  Detail & Related papers  (2023-07-03T08:19:00Z)
• Deep Regression Unlearning [6.884272840652062]
  We introduce deep regression unlearning methods that generalize well and are robust to privacy attacks. We conduct regression unlearning experiments for computer vision, natural language processing and forecasting applications.
  arXiv  Detail & Related papers  (2022-10-15T05:00:20Z)
• Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates [68.09049111171862]
  This work focuses on quantifying, reducing and analyzing regression errors in the NLP model updates. We formulate the regression-free model updates into a constrained optimization problem. We empirically analyze how model ensemble reduces regression.
  arXiv  Detail & Related papers  (2021-05-07T03:33:00Z)
• Reducing Representation Drift in Online Continual Learning [87.71558506591937]
  We study the online continual learning paradigm, where agents must learn from a changing distribution with constrained memory and compute. In this work we instead focus on the change in representations of previously observed data due to the introduction of previously unobserved class samples in the incoming data stream.
  arXiv  Detail & Related papers  (2021-04-11T15:19:30Z)
• Automatic Recall Machines: Internal Replay, Continual Learning and the Brain [104.38824285741248]
  Replay in neural networks involves training on sequential data with memorized samples, which counteracts forgetting of previous behavior caused by non-stationarity. We present a method where these auxiliary samples are generated on the fly, given only the model that is being trained for the assessed objective. Instead the implicit memory of learned samples within the assessed model itself is exploited.
  arXiv  Detail & Related papers  (2020-06-22T15:07:06Z)
• An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation [1.433758865948252]
  We propose a new formalism of knowledge distillation for regression problems. First, we propose a new loss function, teacher outlier loss rejection, which rejects outliers in training samples using teacher model predictions. By considering the multi-task network, training of the feature extraction of student models becomes more effective.
  arXiv  Detail & Related papers  (2020-02-28T08:46:12Z)
• A Graph-Based Approach for Active Learning in Regression [37.42533189350655]
  Active learning aims to reduce labeling efforts by selectively asking humans to annotate the most important data points from an unlabeled pool. Most existing active learning for regression methods use the regression function learned at each active learning iteration to select the next informative point to query. We propose a feature-focused approach that formulates both sequential and batch-mode active regression as a novel bipartite graph optimization problem.
  arXiv  Detail & Related papers  (2020-01-30T00:59:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.