The complexity of feature selection for real-world data stream will increase because of high-dimensional data and concept drifting.

概念流动的出现及数据的高维性增加了数据流特征选择的复杂性。

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Experiments show that comparing majority vote or weight vote ensemble classifiers, stacking ensemble classifiers has stronger ability in adapting to concept drifting and higher accuracy.

实验结果表明，与基于投票或加权投票的集成方法相比，基于堆叠集成方法对概念漂移的快速适应能力以及预测准确率得到了提高。

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The tracking of drifting concept from data streams has recently become one of hot spots in data mining.

数据流上的漂移概念发现已成为数据挖掘领域的研究热点之一。

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