Concept drift is a change of the underlying data distribution which occurs especially with streaming data. Besides other challenges in the field of streaming data classification, concept drift has to be addressed to obtain reliable predictions. Robust Soft Learning Vector Quantization as well as Generalized Learning Vector Quantization has already shown good performance in traditional settings and is modified in this work to handle streaming data. Further, momentum-based stochastic gradient descent techniques are applied to tackle concept drift passively due to increased learning capabilities. The proposed work is tested against common benchmark algorithms and streaming data in the field and achieved promising results.

概念漂移是基础数据分布的变化，尤其是在流数据中。除了流数据分类领域中的其他挑战之外，还必须解决概念漂移问题以获得可靠的预测。稳健的软学习矢量量化以及广义学习矢量量化已经在传统环境中表现出良好的性能，并且在这项工作中进行了修改以处理流数据。此外，由于增加的学习能力，基于动量的随机梯度下降技术可被动地解决概念漂移。拟议的工作经过了通用基准算法和现场流数据的测试，并取得了可喜的成果。