An ensemble Multi-Agent System for non-linear classification

TL;DR

This paper introduces smapy, an ensemble multi-agent system that enhances linear models' ability to perform nonlinear classification tasks through cooperative agent interactions, demonstrated on transport mode detection.

Contribution

It presents a novel ensemble multi-agent framework that enables linear models to effectively handle nonlinear classification problems.

Findings

Linear models' performance improves significantly in nonlinear tasks.

Multi-agent cooperation enhances classification accuracy.

Method is validated on a transport mode detection dataset.

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.