There needs to be an adaptive approach that com-bines both performance and distribution basedconcept drift detectors in order to harness the ben-efits of unlabeled data and the ability to detectvarying types of drifts. This paper proposes Adap-tive Aggregated Drift Detector (A2D2), whichconsists of a suite of performance and data distri-bution based detectors that can adaptively selectdetectors based on rankings of least cost. Thenotable contribution is that it enables an ecosytemto not only adaptively combat drift, but to alsoexpand the information learned across a suite ofdetectors