一种基于原型学习的自适应概念漂移分类方法

为了更准确快速地处理或适应概念漂移,提出了基于原型学习的数据流分类算法,基于发掘并优化现有方法存在的问题,提出了新的方法模型SyncPrototype,在预测方法、原型判定与更新方法等处理概念漂移问题的关键部分做出了新的尝试与优化.实验结果证明,相较于现有方法,SyncPrototype模型在分类性能、概念漂移的响应速度以及时间性能等方面都有明显提高,能够更加有效处理并适应数据流概念漂移问题.

A Prototype-Based Adaptive Concept Drift Classification Method

As a frequent problem that needs to be mainly dealt with in supervised learning scenario of streaming data,the concept drift,primarily,occurs when the data distribution or the target variable changes over time.As typical data streams,the research method which real-time solves or adapts to the concept drift of data streams can provide strong support for grid security dispatch and stable control of realtime decision-making.For accurate and quick dealing with or adapting to concept drift,a prototype-based learning algorithm of data streams classification is discussed.Based on improving the problems which have been explored in existing algorithm,a new algorithm SyncPrototype was proposed,which makes new optimization in terms of methods of classification method,prototype construction and updating.Experiment shows that SyncPrototype can outperforms the existing algorithm in terms of classification performance,time performance and response rate.