In some machine learning applications the availability of labeled instances
for supervised classification is limited while unlabeled instances are
abundant. Semi-supervised learning algorithms deal with these scenarios and
attempt to exploit the information contained in the unlabeled examples. In this
paper, we address the question of how to evolve neural networks for
semi-supervised problems. We introduce neuroevolutionary approaches that
exploit unlabeled instances by using neuron coverage metrics computed on the
neural network architecture encoded by each candidate solution. Neuron coverage
metrics resemble code coverage metrics used to test software, but are oriented
to quantify how the different neural network components are covered by test
instances. In our neuroevolutionary approach, we define fitness functions that
combine classification accuracy computed on labeled examples and neuron
coverage metrics evaluated using unlabeled examples. We assess the impact of
these functions on semi-supervised problems with a varying amount of labeled
instances. Our results show that the use of neuron coverage metrics helps
neuroevolution to become less sensitive to the scarcity of labeled data, and
can lead in some cases to a more robust generalization of the learned
classifiers.

本文介绍了一种在半监督学习中利用神经网络架构内的神经元覆盖度指标来探测未标记样例的神经演化方法，并证实此方法可以使神经进化不再受标记数据稀缺的限制，从而更好地应用在半监督学习中以提高其分类器的泛化能力。

基于神经元覆盖度度量的神经进化算法用于半监督分类

Neuroevolutionary algorithms driven by neuron coverage metrics for semi-supervised classification

Biomedical text tagging systems are plagued by the dearth of labeled training
data. There have been recent attempts at using pre-trained encoders to deal
with this issue. Pre-trained encoder provides representation of the input text
which is then fed to task-specific layers for classification. The entire
network is fine-tuned on the labeled data from the target task. Unfortunately,
a low-resource biomedical task often has too few labeled instances for
satisfactory fine-tuning. Also, if the label space is large, it contains few or
no labeled instances for majority of the labels. Most biomedical tagging
systems treat labels as indexes, ignoring the fact that these labels are often
concepts expressed in natural language e.g. `Appearance of lesion on brain
imaging'. To address these issues, we propose constructing extra labeled
instances using label-text (i.e. label's name) as input for the corresponding
label-index (i.e. label's index). In fact, we propose a number of strategies
for manufacturing multiple artificial labeled instances from a single label.
The network is then fine-tuned on a combination of real and these newly
constructed artificial labeled instances. We evaluate the proposed approach on
an important low-resource biomedical task called \textit{PICO annotation},
which requires tagging raw text describing clinical trials with labels
corresponding to different aspects of the trial i.e. PICO (Population,
Intervention/Control, Outcome) characteristics of the trial. Our empirical
results show that the proposed method achieves a new state-of-the-art
performance for PICO annotation with very significant improvements over
competitive baselines.

本研究提出了一种方法解决生物医学标记系统中标记实例匮乏的问题，该方法使用预训练编码器为任务提供文本表示，并通过使用标签文本作为输入来制造多个人工标记实例。实验结果表明，这种方法在 PICO 注释任务上实现了最新的最佳性能。