Available works addressing multi-label classification in a data stream
environment focus on proposing accurate models; however, these models often
exhibit inefficiency and cannot balance effectiveness and efficiency. In this
work, we propose a neural network-based approach that tackles this issue and is
suitable for high-dimensional multi-label classification. Our model uses a
selective concept drift adaptation mechanism that makes it suitable for a
non-stationary environment. Additionally, we adapt our model to an environment
with missing labels using a simple yet effective imputation strategy and
demonstrate that it outperforms a vast majority of the state-of-the-art
supervised models. To achieve our purposes, we introduce a weighted binary
relevance-based approach named ML-BELS using the Broad Ensemble Learning System
(BELS) as its base classifier. Instead of a chain of stacked classifiers, our
model employs independent weighted ensembles, with the weights generated by the
predictions of a BELS classifier. We show that using the weighting strategy on
datasets with low label cardinality negatively impacts the accuracy of the
model; with this in mind, we use the label cardinality as a trigger for
applying the weights. We present an extensive assessment of our model using 11
state-of-the-art baselines, five synthetics, and 13 real-world datasets, all
with different characteristics. Our results demonstrate that the proposed
approach ML-BELS is successful in balancing effectiveness and efficiency, and
is robust to missing labels and concept drift.

我们提出了基于神经网络的方法来处理高维度多标签分类问题，在数据流环境下通过选择性概念漂移适应机制，使用简单但有效的标签缺失填补策略，基于权重二元关联方法和广义集成学习系统来平衡效果与效率，并通过实验验证了该方法对于缺失标签和概念漂移的鲁棒性。