山东大学学报 (工学版) ›› 2023, Vol. 53 ›› Issue (4): 83-92.doi: 10.6040/j.issn.1672-3961.0.2022.126
• 机器学习与数据挖掘 • 上一篇
张喜龙,韩萌*,陈志强,武红鑫,李慕航
ZHANG Xilong, HAN Meng*, CHEN Zhiqiang, WU Hongxin, LI Muhang
摘要： 鉴于在数据流中无法一次性收集完整的训练集,同时数据可能会处于不平衡状态并夹杂概念漂移而影响分类性能,提出一种在线动态集成选择的不平衡漂移数据流Boosting分类算法。该算法采用多种平衡措施,使用泊松分布对数据流进行重采样,如果数据处于高度不平衡状态则采用存储少数类的窗口进行二次采样以达到当前数据平衡。为了提高算法的处理效率,提出分类器选择集成策略动态调整分类器数目,算法运行过程使用自适应窗口检测器检测概念漂移。试验结果表明,该算法在一定程度上提高了少数类的真阳性率和运行效率,可以对带有概念漂移的不平衡数据流有较好的分类性能。
中图分类号:
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