论文标题
非线性分类的合奏多代理系统
An ensemble Multi-Agent System for non-linear classification
论文作者
论文摘要
自适应多机构系统（AMAS）将机器学习问题转变为代理之间当地合作的问题。我们提出了Smapy，这是一种基于合奏的AMA用于移动性预测的实施，除合作规则外，还提供了机器学习模型。通过详细的方法，我们表明，如果将线性模型集成到合作的多代理结构中，则可以在基准传输模式检测数据集上使用线性模型进行非线性分类。获得的结果表明，由于多代理方法，在非线性环境中线性模型的性能有了显着改善。
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.
