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.
翻译:自我改进多机构系统(AMAS)将机器学习问题转化成代理商之间当地合作的问题。我们展示了基于混合的AMAS实施流动预测的模拟,除合作规则外,还向代理商提供了机器学习模型。我们用一个详细的方法表明,如果将非线性模型纳入一个合作性多机构结构,就可以在基准运输模式探测数据集中使用非线性分类的线性模型。通过多机构方法,所取得的结果表明,线性模型在非线性环境中的性能有了显著改善。