Abstract The amount of real-time communication between agents in an information system has increased rapidly since the beginning of the decade. This is because the use of these systems, e.g. social media, has become commonplace in today’s society. This requires analytical algorithms to learn and predict this stream of information in real-time. The nature of these systems is non-static and can be explained, among other things, by the fast pace of trends. This creates an environment in which algorithms must recognize changes and adapt. Recent work shows vital research in the field, but mainly lack stable performance during model adaptation. In this work, a concept drift detection strategy followed by a prototype-based adaptation strategy is proposed. Validated through experimental results on a variety of typical non-static data, our solution provides stable and quick adjustments in times of change.

中文翻译：

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概念漂移流的反应式软原型计算

摘要 信息系统中代理之间的实时通信量自本世纪初以来迅速增加。这是因为这些系统（例如社交媒体）的使用在当今社会已变得司空见惯。这需要分析算法来实时学习和预测这一信息流。这些系统的性质是非静态的，除其他外，可以通过趋势的快节奏来解释。这创造了一个算法必须识别变化并适应的环境。最近的工作显示了该领域的重要研究，但主要是在模型适应过程中缺乏稳定的性能。在这项工作中，提出了一种概念漂移检测策略，然后是基于原型的适应策略。通过对各种典型非静态数据的实验结果验证，