@inproceedings {pub3363,
	title = {SAM: How to Deal with Diverse Drift Types},
	author = {Viktor Losing AND Barbara Hammer AND Heiko Wersing},
	year = {2017},
	abstract = {Data Mining in non-stationary data streams is particularly relevant  in the context of Internet of Things and Big Data. Its challenges arise from fundamentally different drift types violating assumptions of data independence or stationarity. Available methods often struggle with certain forms of drift or require unavailable a priori task knowledge. We propose the Self Adjusting Memory (SAM) model for the k Nearest Neighbor (kNN) algorithm. SAM-kNN can deal with heterogeneous concept drift, i.e different drift types and rates, using biologically inspired memory models and their coordination. Its basic idea are dedicated models for current and former concepts used according to the demands of the given situation. It can be robustly applied in practice without meta parameter optimization.
We conduct an extensive evaluation on various benchmarks, consisting of artificial streams with known drift characteristics and real world datasets. We explicitly add new benchmarks enabling a precise performance analysis on multiple types of drift. Highly competitive results throughout all experiments underline the robustness of SAM-kNN as well as its capability to handle heterogeneous concept drift.},
	publisher = { AAAI Press},
	booktitle = {International Joint Conference on Artificial Intelligence},
	city = {Melbourne}
}