A computational intelligence-based criterion to detect non-stationarity trends

The stationarity hypothesis is largely and implicitly assumed when designing classifiers (especially those for industrial applications) but it does not generally hold in practice. The paper goal is to provide an automatic, general purpose, easy to use and effective index for estimating deviations, drifts or ageing effects in the process generating the data (e.g., classifier inputs); in turns this will allow the designer for identifying when to intervene to update the knowledge space of adaptive classifiers. More specifically, we suggest a robust extension of the adaptive CUSUM test procedure which addresses a set of features (in contrast to the literature which considers a single feature) for detecting drifts. The application of the change detection test to real applications shows that its real additional value resides in the ability to detect continuous and small drifts, a critical situation for traditional tests.