A framework for application-driven classification of data streams

被引:12
|
作者
Zhang, Peng [1 ,2 ]
Gao, Byron J. [2 ]
Liu, Ping [1 ]
Shi, Yong [3 ]
Guo, Li [1 ]
机构
[1] Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
[2] Texas State Univ, Dept Comp Sci, San Marcos, TX 78666 USA
[3] Chinese Acad Sci, FEDS Ctr, Grad Univ, Beijing 100190, Peoples R China
基金
美国国家科学基金会;
关键词
Data stream classification; Transfer learning; Semi-supervised learning; Relational k-means; Concept drifting;
D O I
10.1016/j.neucom.2011.11.026
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Data stream classification has drawn increasing attention from the data mining community in recent years. Relevant applications include network traffic monitoring, sensor network data analysis, Web click stream mining, power consumption measurement, dynamic tracing of stock fluctuations, to name a few. Data stream classification in such real-world applications is typically subject to three major challenges: concept drifting, large volumes, and partial labeling. As a result, training examples in data streams can be very diverse and it is very hard to learn accurate models with efficiency. In this paper, we propose a novel framework that first categorizes diverse training examples into four types and assign learning priorities to them. Then, we derive four learning cases based on the proportion and priority of the different types of training examples. Finally, for each learning case, we employ one of the four SVM-based training models: classical SVM, semi-supervised SVM, transfer semi-supervised SVM, and relational k-means transfer semi-supervised SVM. We perform comprehensive experiments on real-world data streams that validate the utility of our approach. (c) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:170 / 182
页数:13
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