  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.
