面向低资源数据垃圾短信检测的条件半监督数据增强
Conditional Semi-Supervised Data Augmentation for Spam Message Detection with Low Resource Data
AI总结：
针对低资源垃圾短信检测中标注数据不足的问题，提出CSSDA框架，利用条件生成网络从未标注数据构造潜变量以增强训练，并在实验中取得约85%的平衡准确率。
摘要：
多种机器学习方案已尝试执行垃圾短信检测。然而，这些方案大多需要大量标注数据。现有解决数据可用性不足的技术在有效性和鲁棒性方面存在问题。因此，本文针对缺乏数据可用性的垃圾短信检测模型提出了一种条件半监督数据增强（CSSDA）方法。CSSDA的主体架构由特征提取和增强生成网络组成。在此，我们利用未标注数据进行数据增强，以扩展训练数据。我们所提方案中的增强生成模块通过一种条件方案，从未标注数据生成作为伪样本的潜变量。潜变量可来自标注数据和未标注数据，作为垃圾短信检测模型中最终分类器的输入。实验结果表明，与若干利用未标注数据和不利用未标注数据的相关方法相比，我们提出的CSSDA取得了优异结果。在使用不同数量未标注数据的实验阶段，当标注数据可用性较大时，CSSDA是唯一取得约85%平衡准确率的鲁棒模型。我们还进行了若干消融研究，以详细考察所提方案。结果还表明，多项消融研究强化了我们提出的创新点。这些实验表明，对于垃圾短信检测中采用条件半监督方案的数据增强，未标注数据具有显著贡献。
英文摘要：
Several machine learning schemes have attempted to perform the detection of spam messages. However, those schemes mostly require a huge amount of labeled data. The existing techniques addressing the lack of data availability have issues with effectiveness and robustness. Therefore, this paper proposes a conditional semi-supervised data augmentation (CSSDA) for a spam detection model lacking the availability of data. The main architecture of CSSDA comprises feature extraction and enhanced generative network. Here, we exploit unlabeled data for data augmentation to extend training data. The enhanced generative in our proposed scheme produces latent variables as fake samples from unlabeled data through a conditional scheme. Latent variables can come from labeled and unlabeled data as the input for the final classifier in our spam detection model. The experimental results indicate that our proposed CSSDA achieves excellent results compared to several related methods both exploiting unlabeled data and not. In the experiment stage with various amounts of unlabeled data, CSSDA is the only robust model that obtains a balanced accuracy of about 85% when the availability of labeled data is large. We also conduct several ablation studies to investigate our proposed scheme in detail. The result also shows that several ablation studies strengthen our proposed innovations. These experiments indicate that unlabeled data has a significant contribution to data augmentation using the conditional semi-supervised scheme for spam detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
