Is augmentation effective to improve prediction in imbalanced text datasets?

• URL: http://arxiv.org/abs/2304.10283v1
• Date: Thu, 20 Apr 2023 13:07:31 GMT
• Title: Is augmentation effective to improve prediction in imbalanced text datasets?
• Authors: Gabriel O. Assun\c{c}\~ao and Rafael Izbicki and Marcos O. Prates
• Abstract summary: We argue that adjusting the cutoffs without data augmentation can produce similar results to oversampling techniques. Our findings contribute to a better understanding of the strengths and limitations of different approaches to dealing with imbalanced data.
• Score: 3.1690891866882236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Imbalanced datasets present a significant challenge for machine learning models, often leading to biased predictions. To address this issue, data augmentation techniques are widely used in natural language processing (NLP) to generate new samples for the minority class. However, in this paper, we challenge the common assumption that data augmentation is always necessary to improve predictions on imbalanced datasets. Instead, we argue that adjusting the classifier cutoffs without data augmentation can produce similar results to oversampling techniques. Our study provides theoretical and empirical evidence to support this claim. Our findings contribute to a better understanding of the strengths and limitations of different approaches to dealing with imbalanced data, and help researchers and practitioners make informed decisions about which methods to use for a given task.

Related papers

• Learning Augmentation Policies from A Model Zoo for Time Series Forecasting [58.66211334969299]
  We introduce AutoTSAug, a learnable data augmentation method based on reinforcement learning. By augmenting the marginal samples with a learnable policy, AutoTSAug substantially improves forecasting performance.
  arXiv  Detail & Related papers  (2024-09-10T07:34:19Z)
• Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance [16.047084318753377]
  Imbalanced data and spurious correlations are common challenges in machine learning and data science. Oversampling, which artificially increases the number of instances in the underrepresented classes, has been widely adopted to tackle these challenges. We introduce OPAL, a systematic oversampling approach that leverages the capabilities of large language models to generate high-quality synthetic data for minority groups.
  arXiv  Detail & Related papers  (2024-06-05T21:24:26Z)
• Multi-Source Conformal Inference Under Distribution Shift [41.701790856201036]
  We consider the problem of obtaining distribution-free prediction intervals for a target population, leveraging multiple potentially biased data sources. We derive the efficient influence functions for the quantiles of unobserved outcomes in the target and source populations. We propose a data-adaptive strategy to upweight informative data sources for efficiency gain and downweight non-informative data sources for bias reduction.
  arXiv  Detail & Related papers  (2024-05-15T13:33:09Z)
• Do We Really Even Need Data? [2.3749120526936465]
  Researchers increasingly use predictions from pre-trained algorithms as outcome variables. Standard tools for inference can misrepresent the association between independent variables and the outcome of interest when the true, unobserved outcome is replaced by a predicted value.
  arXiv  Detail & Related papers  (2024-01-14T23:19:21Z)
• On Counterfactual Data Augmentation Under Confounding [30.76982059341284]
  Counterfactual data augmentation has emerged as a method to mitigate confounding biases in the training data. These biases arise due to various observed and unobserved confounding variables in the data generation process. We show how our simple augmentation method helps existing state-of-the-art methods achieve good results.
  arXiv  Detail & Related papers  (2023-05-29T16:20:23Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• A Guide for Practical Use of ADMG Causal Data Augmentation [0.0]
  Causal data augmentation strategies have been pointed out as a solution to handle these challenges. This paper experimentally analyzed the ADMG causal augmentation method considering different settings.
  arXiv  Detail & Related papers  (2023-04-03T09:31:13Z)
• Automatic Data Augmentation via Invariance-Constrained Learning [94.27081585149836]
  Underlying data structures are often exploited to improve the solution of learning tasks. Data augmentation induces these symmetries during training by applying multiple transformations to the input data. This work tackles these issues by automatically adapting the data augmentation while solving the learning task.
  arXiv  Detail & Related papers  (2022-09-29T18:11:01Z)
• Negative Data Augmentation [127.28042046152954]
  We show that negative data augmentation samples provide information on the support of the data distribution. We introduce a new GAN training objective where we use NDA as an additional source of synthetic data for the discriminator. Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities.
  arXiv  Detail & Related papers  (2021-02-09T20:28:35Z)
• On the Benefits of Invariance in Neural Networks [56.362579457990094]
  We show that training with data augmentation leads to better estimates of risk and thereof gradients, and we provide a PAC-Bayes generalization bound for models trained with data augmentation. We also show that compared to data augmentation, feature averaging reduces generalization error when used with convex losses, and tightens PAC-Bayes bounds.
  arXiv  Detail & Related papers  (2020-05-01T02:08:58Z)
• Generative Data Augmentation for Commonsense Reasoning [75.26876609249197]
  G-DAUGC is a novel generative data augmentation method that aims to achieve more accurate and robust learning in the low-resource setting. G-DAUGC consistently outperforms existing data augmentation methods based on back-translation. Our analysis demonstrates that G-DAUGC produces a diverse set of fluent training examples, and that its selection and training approaches are important for performance.
  arXiv  Detail & Related papers  (2020-04-24T06:12:10Z)

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.