  Self-Adaptive Multi-Agent Systems (AMAS) transform machine learning problems
into problems of local cooperation between agents. We present smapy, an
ensemble based AMAS implementation for mobility prediction, whose agents are
provided with machine learning models in addition to their cooperation rules.
With a detailed methodology, we show that it is possible to use linear models
for nonlinear classification on a benchmark transport mode detection dataset,
if they are integrated in a cooperative multi-agent structure. The results
obtained show a significant improvement of the performance of linear models in
non-linear contexts thanks to the multi-agent approach.
