增强AI功率：电子鼻鉴别中未标记数据与共形预测的数据增强策略
Boost AI Power: Data Augmentation Strategies with unlabelled Data and Conformal Prediction, a Case in Alternative Herbal Medicine Discrimination with Electronic Nose
摘要：
电子鼻已被证明是有效的替代草药分类，但由于监督学习的性质，以前的研究严重依赖于标记的训练数据，这是耗时和劳动密集型的收集，以减轻在现实世界中的应用程序对训练数据的关键依赖，本研究旨在提高分类精度通过数据增强策略。在两种情况下，不同的训练数据不足的五个数据增强策略的有效性进行了研究：无噪声的情况下，不同的可用性的未标记的数据被认为是，和噪声的情况下，不同水平的高斯噪声和平移位移被添加到代表传感器漂移。五个增强策略，即噪声添加数据增强，半监督学习，基于分类器的在线学习，归纳共形预测（ICP）在线学习和我们的新的集成ICP在线学习提出了这项研究中，实验和比较监督学习基线，线性判别分析（LDA）和支持向量机（SVM）作为分类器。我们的新策略，集成ICP在线学习，通过在所有任务上显示出非降低的分类准确性和在大多数模拟任务上的显著改善（36个任务中的25个，p<=0.05），优于其他策略。此外，本研究还对不同的增强策略进行了系统的分析。结果表明，在每个任务中，至少一种策略显著提高了LDA的分类准确率（p<=0.05），并且不降低SVM的分类准确率。特别是，我们提出的策略在提高分类模型的泛化能力方面表现出了有效性和鲁棒性，这可以用于其他机器学习应用。
英文摘要：
Electronic nose has been proven to be effective in alternative herbal medicine classification, but due to the nature of supervised learning, previous research heavily relies on the labelled training data, which are time-costly and labor-intensive to collect. To alleviate the critical dependency on the training data in real-world applications, this study aims to improve classification accuracy via data augmentation strategies. The effectiveness of five data augmentation strategies under different training data inadequacy are investigated in two scenarios: the noise-free scenario where different availabilities of unlabelled data were considered, and the noisy scenario where different levels of Gaussian noises and translational shifts were added to represent sensor drifts. The five augmentation strategies, namely noise-adding data augmentation, semi-supervised learning, classifier-based online learning, Inductive Conformal Prediction (ICP) online learning and our novel ensemble ICP online learning proposed in this study, are experimented and compared against supervised learning baseline, with Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) as the classifiers. Our novel strategy, ensemble ICP online learning, outperforms the others by showing non-decreasing classification accuracy on all tasks and a significant improvement on most simulated tasks (25out of 36 tasks,p<=0.05). Furthermore, this study provides a systematic analysis of different augmentation strategies. It shows at least one strategy significantly improved the classification accuracy with LDA (p<=0.05) and non-decreasing classification accuracy with SVM in each task. In particular, our proposed strategy demonstrated both effectiveness and robustness in boosting the classification model generalizability, which can be employed in other machine learning applications.
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