Design of ensemble classifiers for mining concept drifts from data streams
数据流中概念漂移检测的集成分类器设计*

A new mining algorithm called ICEA was proposed for mining concept drifts from data streams, which used ensemble multi-classifiers to detect concept changes from the data streams in an incremental way. The experimental results show that ICEA performs higher accuracy and better time efficiency on mining concept drifts from data streams.