Data Mining in non-stationary data streams is gaining more attention

recently, especially in the context of Internet of Things and Big Data.

It is a highly challenging task, since the fundamentally different types

of possibly occurring drift undermine classical assumptions such as

data independence or stationary distributions.

Available algorithms are either struggling with certain forms of drift or require

a priori knowledge in terms of a task specific setting.

We propose the Self Adjusting Memory (SAM) model for the k Nearest Neighbor (kNN) algorithm

since kNN constitutes a proven classifier within the streaming setting.

SAM-kNN can deal with heterogeneous concept drift,

i.e different drift types and rates, using biologically inspired

memory models and their coordination. It can be easily

applied in practice since an optimization of the meta parameters is not necessary.

The basic idea is to construct dedicated models for the

current and former concepts and apply them according to

the demands of the given situation.

An extensive evaluation on various benchmarks, consisting of artificial streams

with known drift characteristics as well as real world datasets is conducted.

Thereby, we explicitly add new benchmarks enabling a precise performance evaluation

on multiple types of drift. The highly competitive results throughout all

experiments underline the robustness of SAM-kNN as well as its capability

to handle heterogeneous concept drift.