An ensemble Multi-Agent System for non-linear classification

Autor:	Fourez, Thibault, Verstaevel, Nicolas, Migeon, Frédéric, Schettini, Frédéric, Amblard, Frederic
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Multiagent Systems
Zdroj:	14th ITS European Congress, May 2022, Toulouse, France
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2209.06824 Zobrazit plný text záznamu View this record from Arxiv