图 书 馆
进化数据流的新集成方法New Ensemble Methods for Evolving Data Streams |
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| 课程网址: | http://videolectures.net/kdd09_bifet_nemeds/ |
| 主讲教师: | Albert Bifet |
| 开课单位: | Telecom ParisTech |
| 开课时间: | 2009-09-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Advanced analysis of data streams is quickly becoming a key area of data mining research as the number of applications demanding such processing increases. Online mining when such data streams evolve over time, that is when concepts drift or change completely, is becoming one of the core issues. When tackling non-stationary concepts, ensembles of classifiers have several advantages over single classifier methods: they are easy to scale and parallelize, they can adapt to change quickly by pruning under-performing parts of the ensemble, and they therefore usually also generate more accurate concept descriptions. This paper proposes a new experimental data stream framework for studying concept drift, and two new variants of Bagging: ADWIN Bagging and Adaptive-Size Hoeffding Tree (ASHT) Bagging. Using the new experimental framework, an evaluation study on synthetic and real-world datasets comprising up to ten million examples shows that the new ensemble methods perform very well compared to several known methods. |
| 关 键 词: | 数据流处理; 高级分析; 实验框架 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-07：chenjy |
| 最后编审: | 2023-03-07：chenjy |
| 阅读次数: | 26 |