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An ensemble Multi-Agent System for non-linear classification

Abstract : 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.
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Contributor : Thibault Fourez Connect in order to contact the contributor
Submitted on : Tuesday, September 13, 2022 - 3:25:20 PM
Last modification on : Friday, September 16, 2022 - 10:14:17 AM


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  • HAL Id : hal-03776269, version 1


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⟩



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