An ensemble Multi-Agent System for non-linear classification

Because of this non-linearity, their resolution requires more complex models often called "black boxes" because of their low explicability. In our research project, we aim to design a method to predict mobility information such as users' transport mode in real time from heterogeneous data (e.g., mobile phone data, smartphone sensors, etc.). This method must adapt quickly in a dynamic system where new transport modes and perturbations (e.g., changes in speed limits, COVID-19, etc.) may appear. Bringing up ever larger data streams requires the adoption of online learning techniques in which the model is updated with each new labeled point. Machine learning on dynamic systems (i.e., in which the behavior of individuals, the available sensors and the classes can evolve continuously) is one of the main motivations behind the design of Multi-Agent Systems (MAS). Recent approaches propose to transform a machine learning problem into a problem of cooperation between agents in order to reduce its complexity and to allow the system to adapt to the evolutions of the individuals (Capera et al., 2003). In this paper, we propose to use this collaborative approach to design an algorithm capable of solving supervised classification problems, some of which are non-linear, using linear classification models embedded in a multi-agent structure.