We consider a cooperative learning scenario where a collection of networked agents with individually owned classifiers dynamically update their predictions, for the same classification task, through communication or observations of each other's predictions. Clearly if highly influential vertices use erroneous classifiers, there will be a negative effect on the accuracy of all the agents in the network. We ask the following question: how can we optimally fix the prediction of a few classifiers so as maximize the overall accuracy in the entire network. To this end we consider an aggregate and an egalitarian objective function. We show a polynomial time algorithm for optimizing the aggregate objective function, and show that optimizing the egalitarian objective function is NP-hard. Furthermore, we develop approximation algorithms for the egalitarian improvement. The performance of all of our algorithms are guaranteed by mathematical analysis and backed by experiments on synthetic and real data.

翻译：我们考虑一种合作学习场景：一组拥有各自分类器的网络化智能体，通过通信或观察彼此的预测，动态更新针对同一分类任务的预测结果。显然，若影响力高的节点使用错误分类器，将对网络中所有智能体的准确性产生负面影响。我们提出以下问题：如何以最优方式修正少数分类器的预测，从而最大化整个网络的整体准确性？为此，我们考虑了一种聚合目标函数和一种平等主义目标函数。我们证明了优化聚合目标函数存在多项式时间算法，并证明优化平等主义目标函数是NP难问题。此外，我们为平等主义改进设计了近似算法。所有算法的性能均通过数学分析得到保证，并基于合成数据与真实数据的实验验证。