基于随机标记子集的多标记数据流分类算法 |
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引用本文: | 孙艳歌,尤磊,卲罕,李艳灵.基于随机标记子集的多标记数据流分类算法[J].信阳师范学院学报(自然科学版),2018(1):119-123. |
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作者姓名: | 孙艳歌 尤磊 卲罕 李艳灵 |
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作者单位: | 信阳师范学院计算机与信息技术学院; |
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摘 要: | 提出了基于随机标记子集的多标记数据流分类算法,其基本思想是在多标记分类过程中,将原始较大的标记集随机地划分为多个较小的标记子集,并针对每个标记子集训练一个概率分类器链.在充分利用标记间依赖关系的同时,又有效地降低了概率分类器链的时间复杂度.同时,在算法中嵌入了自适应滑动窗口算法来检测概念漂移.实验结果表明,同其他算法相比,在大多数数据集合上能够更有效地预测实例的类标集合,更适合概念漂移的环境.
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关 键 词: | 数据流 多标记 集成学习 概念漂移 依赖关系 |
Classification for Multi-label Data Streams Based on Random Labelsets |
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Institution: | ,College of Computer and Information Technology,Xinyang Normal University |
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Abstract: | To address the issue of concept drift,on the basis of considering the dependency between labels,a novel ensemble classifier was introduced based on random labelsets for multi-label data streams.First,it divided the label set into several subsets based on RAkEL algorithm.Then a classifier on each subset was built using probabilistic classifier chain.Moreover,the adaptive windowing algorithm as a change detector was used to deal with concept drift.The experimental results on both synthetic and real-world data streams showed that our method achieves better performance than the previous methods,especially in datasets with concept drifts. |
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Keywords: | data streams multi-label ensemble learning concept drift label dependency |
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