An ensemble Multi-Agent System for non-linear classification

Un système multi-agents ensembliste pour la classification non linéaire

Thibault Fourez (1, 2) , Nicolas Verstaevel (1) , Frédéric Migeon (1) , Frédéric Schettini (2) , Frederic Amblard (1)

1 IRIT-SMAC - Systèmes Multi-Agents Coopératifs
2 CITEC - Groupe CITEC

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.

Domains: Artificial Intelligence [cs.AI]; Multiagent Systems [cs.MA]; Machine Learning [stat.ML]

Thibault Fourez, Nicolas Verstaevel, Frédéric Migeon, Frédéric Schettini, Frederic Amblard. An ensemble Multi-Agent System for non-linear classification. 14th ITS European Congress (ITS EU 2022), May 2022, Toulouse, France. pp.1-12. ⟨hal-03776269⟩