基于反事实推理和主动学习的高效分类

A. Mohammed, D. Nguyen, Bao Duong, T. Nguyen
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引用次数: 0

摘要

数据增强是计算机视觉中提高机器学习模型分类精度最成功的技术之一。然而,将数据增强应用于表格数据是一个具有挑战性的问题,因为很难生成带有标签的合成样本。在本文中,我们提出了一个有效的分类器与新的数据增强技术的表格数据。我们的CCRAL方法结合了因果推理来学习原始训练样本的反事实样本和基于不确定区域的主动学习来选择有用的反事实样本。通过这样做,我们的方法可以最大化我们的模型对未知测试数据的泛化。我们分析验证了我们的方法,并与标准基线进行了比较。我们的实验结果表明,在精度和AUC方面,CCRAL在几个真实表格数据集上取得了明显优于基线的性能。数据和源代码可从https://github.com/nphdang/CCRAL获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient Classification with Counterfactual Reasoning and Active Learning
Data augmentation is one of the most successful techniques to improve the classification accuracy of machine learning models in computer vision. However, applying data augmentation to tabular data is a challenging problem since it is hard to generate synthetic samples with labels. In this paper, we propose an efficient classifier with a novel data augmentation technique for tabular data. Our method called CCRAL combines causal reasoning to learn counterfactual samples for the original training samples and active learning to select useful counterfactual samples based on a region of uncertainty. By doing this, our method can maximize our model's generalization on the unseen testing data. We validate our method analytically, and compare with the standard baselines. Our experimental results highlight that CCRAL achieves significantly better performance than those of the baselines across several real-world tabular datasets in terms of accuracy and AUC. Data and source code are available at: https://github.com/nphdang/CCRAL.
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