Passive concept drift handling via variations of learning vector quantization

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.

Automated summary

This work modifies robust soft learning vector quantization and generalized learning vector quantization to handle concept drift in streaming data and applies momentum-based stochastic gradient descent techniques. Tested against common benchmark algorithms and streaming data in the field, the proposed work achieved promising results.