An ensemble Multi-Agent System for non-linear classification
This work addresses mobility prediction challenges for transportation systems, but it appears incremental as it builds on existing multi-agent frameworks.
The paper tackled the problem of using linear models for nonlinear classification in mobility prediction by integrating them into a cooperative multi-agent system, resulting in significant performance improvements on a transport mode detection dataset.
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.