论文标题
如何使用合作瓷砖多代理系统解决分类问题？
How to solve a classification problem using a cooperative tiling Multi-Agent System?
论文作者
论文摘要
自适应多代理系统（AMA）将动态问题转化为代理之间局部合作的问题。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，我们提出了一个框架，将分类问题转换为输入变量空间的合作瓷砖。我们表明，如果将线性分类器用于在线非线性分类，以在三个基准的玩具问题上为它们的不同线性可分离性选择，如果它们集成到合作的多机构结构中。获得的结果表明，由于合作方法，在非线性环境中，线性分类器在非线性上下文中的性能有了显着改善。
Adaptive Multi-Agent Systems (AMAS) transform dynamic 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 propose a framework to transform a classification problem into a cooperative tiling of the input variable space. We show that it is possible to use linear classifiers for online non-linear classification on three benchmark toy problems chosen for their different levels of linear separability, if they are integrated in a cooperative Multi-Agent structure. The results obtained show a significant improvement of the performance of linear classifiers in non-linear contexts in terms of classification accuracy and decision boundaries, thanks to the cooperative approach.
