Data Scientist

Concept Drift: Learning From Non-Stationary Distributions

The world is dynamic, in a constant state of flux, while machine learning systems typically learn static models from historical data. Failure to account for the dynamic nature of the world may result in sub-optimal performance when these models of the past are used to predict the present or future. This research investigates this phenomenon of concept drift and how it is best addressed.

Our software for generating synthetic data streams with abrupt drift can be downloaded here.

Our system for describing the concept drift present in real-world data can be downloaded here.