We focus on the online-based active learning (OAL) setting where an agent
operates over a stream of observations and trades-off between the costly
acquisition of information (labelled observations) and the cost of prediction
errors. We propose a novel foundation for OAL tasks based on partial
monitoring, a theoretical framework specialized in online learning from
partially informative actions. We show that previously studied binary and
multi-class OAL tasks are instances of partial monitoring. We expand the
real-world potential of OAL by introducing a new class of cost-sensitive OAL
tasks. We propose NeuralCBP, the first PM strategy that accounts for predictive
uncertainty with deep neural networks. Our extensive empirical evaluation on
open source datasets shows that NeuralCBP has favorable performance against
state-of-the-art baselines on multiple binary, multi-class and cost-sensitive
OAL tasks.

在线主动学习（OAL）以在线学习从部分信息动作中进行信息获取（标记的观察）和预测错误的成本之间的权衡为基础，通过提出基于部分监控的新型 OAL 任务，以及首个考虑深度神经网络的预测不确定性的 PM 策略 NeuralCBP，拓宽了现实世界中 OAL 的潜力。基于对多个二元、多分类和成本敏感 OAL 任务的多个开源数据集的广泛实证评估，证明了 NeuralCBP 在性能上超越了最先进的基准模型。

神经主动学习与部分监督框架相遇

Neural Active Learning Meets the Partial Monitoring Framework

Cost-Sensitive Online Classification has drawn extensive attention in recent
years, where the main approach is to directly online optimize two well-known
cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii)
weighted misclassification cost. However, previous existing methods only
considered first-order information of data stream. It is insufficient in
practice, since many recent studies have proved that incorporating second-order
information enhances the prediction performance of classification models. Thus,
we propose a family of cost-sensitive online classification algorithms with
adaptive regularization in this paper. We theoretically analyze the proposed
algorithms and empirically validate their effectiveness and properties in
extensive experiments. Then, for better trade off between the performance and
efficiency, we further introduce the sketching technique into our algorithms,
which significantly accelerates the computational speed with quite slight
performance loss. Finally, we apply our algorithms to tackle several online
anomaly detection tasks from real world. Promising results prove that the
proposed algorithms are effective and efficient in solving cost-sensitive
online classification problems in various real-world domains.

本文提出了一类基于自适应正则化的代价敏感在线分类算法，并将描绘技术引入到算法中以提高效率。通过针对真实世界的在线异常检测任务的应用，证明了该算法在各种领域中解决代价敏感在线分类问题的有效性和效率。

自适应的代价敏感的在线分类

Adaptive Cost-sensitive Online Classification

Label space expansion for multi-label classification (MLC) is a methodology
that encodes the original label vectors to higher dimensional codes before
training and decodes the predicted codes back to the label vectors during
testing. The methodology has been demonstrated to improve the performance of
MLC algorithms when coupled with off-the-shelf error-correcting codes for
encoding and decoding. Nevertheless, such a coding scheme can be complicated to
implement, and cannot easily satisfy a common application need of
cost-sensitive MLC---adapting to different evaluation criteria of interest. In
this work, we show that a simpler coding scheme based on the concept of a
reference pair of label vectors achieves cost-sensitivity more naturally. In
particular, our proposed cost-sensitive reference pair encoding (CSRPE)
algorithm contains cluster-based encoding, weight-based training and
voting-based decoding steps, all utilizing the cost information. Furthermore,
we leverage the cost information embedded in the code space of CSRPE to propose
a novel active learning algorithm for cost-sensitive MLC. Extensive
experimental results verify that CSRPE performs better than state-of-the-art
algorithms across different MLC criteria. The results also demonstrate that the
CSRPE-backed active learning algorithm is superior to existing algorithms for
active MLC, and further justify the usefulness of CSRPE.

该研究论文介绍了一种基于参考标签向量的经济敏感多标签分类算法，其中利用了集群编码、权重训练和投票解码等步骤，以及嵌入于其码空间中的成本信息，提出了一种新颖的经济敏感主动学习算法，实验结果表明，该算法优于现有算法。