概念漂移数据流分类研究综述

A survey of the classification of data streams with concept drift

由于现有各种机器学习算法本质上都基于一个静态学习环境,而以尽量保证学习系统泛化能力为目标的寻优过程,概念漂移数据流分类给机器学习带来了巨大挑战.从数据流与概念漂移、概念漂移数据流分类研究的发展与趋势、概念漂移数据流分类的主要研究领域、概念漂移数据流分类研究的新动态4个方面展开了文献综述,并分析了当前概念漂移数据流分类算法存在的问题.

Because the current machine learning algorithms all are essentially an optimization procedure that aims to ensure the generalization ability based on static learning environment, the classification data streams with concept drift has brought severe challenges to machine learning. In order to address these concerns, a survey was developed consisting of four aspects： the introduction to data streams and concept drift, the development process and future trends, the main research fields, and the new developments in the study field of the classification data streams with concept drift. The existing problems relating to classification data streams with concept drift were discussed at last.