图 书 馆
基于原型的概念漂移数据流学习Prototype-based Learning on Concept-drifting Data Streams |
|
| 课程网址: | http://videolectures.net/kdd2014_ahmadi_learning/ |
| 主讲教师: | Zahra Ahmadi |
| 开课单位: | 古滕伯格大学 |
| 开课时间: | 2014-10-07 |
| 课程语种: | 英语 |
| 中文简介: | 数据流挖掘由于其广泛的新兴应用（例如目标市场营销，电子邮件过滤和网络入侵检测）而受到越来越多的关注。在本文中，我们提出了一种用于演进数据流的基于原型的分类模型，称为SyncStream，该模型动态地对时变概念进行建模，并以本地方式进行预测。 SyncStream无需在滑动窗口上学习单个模型或进行整体学习，而是通过在称为P树的新数据结构中动态维护一组原型来捕获不断发展的概念。原型是通过错误驱动的代表性学习和同步启发式约束聚类获得的。为了识别数据流中的突然概念漂移，采用了PCA和基于统计的启发式方法。 SyncStream具有几个吸引人的好处：（a）能够动态建模甚至是一小组原型中不断发展的概念，并且对于嘈杂的示例具有鲁棒性。 （b）由于基于同步的约束聚类和P树，它支持高效有效的数据表示和维护。 （c）可以有效地检测到概念的逐渐和突然的漂移。实验结果表明，与现有算法相比，该方法具有良好的预测性能，并且比基于实例的流挖掘算法所需的时间短得多。 |
| 课程简介: | Data stream mining has gained growing attentions due to its wide emerging applications such as target marketing, email filtering and network intrusion detection. In this paper, we propose a prototype-based classification model for evolving data streams, called SyncStream, which dynamically models time-changing concepts and makes predictions in a local fashion. Instead of learning a single model on a sliding window or ensemble learning, SyncStream captures evolving concepts by dynamically maintaining a set of prototypes in a new data structure called the P-tree. The prototypes are obtained by error-driven representativeness learning and synchronization-inspired constrained clustering. To identify abrupt concept drift in data streams, PCA and statistics based heuristic approaches are employed. SyncStream has several attractive benefits: (a) It is capable of dynamically modeling evolving concepts from even a small set of prototypes and is robust against noisy examples. (b) Owing to synchronization-based constrained clustering and the P-Tree, it supports an efficient and effective data representation and maintenance. (c) Gradual and abrupt concept drift can be effectively detected. Empirical results shows that our method achieves good predictive performance compared to state-of-the-art algorithms and that it requires much less time than another instance-based stream mining algorithm. |
| 关 键 词: | 数据流挖掘; 时变概念建模; 代表性学习; 动态建模 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-05-27：zyk |
| 最后编审: | 2021-05-27：zyk |
| 阅读次数: | 50 |