Concept drift visualization

被引：0
作者：
机构：
[1] School of Computer Science and Technology, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology
[2] School of Innovation and Experiment, Dalian University of Technology
来源：
Yao, Y. (yaodoctor@gmail.com) | 1600年 / Binary Information Press, Flat F 8th Floor, Block 3, Tanner Garden, 18 Tanner Road, Hong Kong卷 / 10期
关键词：
Concept drift; Concept pool; Data stream; KL-divergence; Visualization;
D O I：
10.12733/jics20101915
中图分类号：
学科分类号：
摘要：
Mining data stream are facing many challenges now, one of them is concept drift problem. In many practical applications, concept drift usually affects the classification performance for data stream, or even make the classifier failed. However, most of the proposed methods are mainly focusing on solving concept drift from the data value point of view, and very little attention has been focused on mining the knowledge in the data concept level. Motivated by this, in this paper, we use Kullback-Leibler divergence (KL-divergence) algorithm to detect concept drift dynamically. Meanwhile, we also construct a concept pool to reserve distinct concepts in data stream and analyze the concept transformation information. Experimental studies on two real-world data sets demonstrate that the proposed concept visualization method and concept transformation map could effectively and efficiently mine concept drifts relationship from the noisy streaming data. Copyright © 2013 Binary Information Press.
引用
收藏
页码：3021 / 3029
页数：8
相关论文
共 12 条
  • [1] Alzghoul A., Lofstrand M., Backe B., Data stream forecasting for system fault prediction, Computers and Industrial Engineering, 4, pp. 972-978, (2012)
  • [2] Borchani H., Larranaga P., Bielza C., Classifying evolving data streams with partially labeled data, Intelligent Data Analysis, 5, pp. 655-670, (2011)
  • [3] da Silva A., Chiky R., Hebrail G., A clustering approach for sampling data streams in sensor networks, Knowledge and Information Systems, 1, pp. 1-23, (2012)
  • [4] Elwell R., Polikar R., Incremental learning of concept drift in nonstationary environments, IEEE Transactions on Neural Networks, 10, pp. 1517-1531, (2011)
  • [5] Ikonomovska E., Gama J., Dzeroski S., Learning model trees from evolving data streams, Data Mining and Knowledge Discovery, 1, pp. 128-168, (2011)
  • [6] Kullback S., Leibler R.A., On information and sufficiency, The Annals of Mathematical Statistics, 1, pp. 79-86, (1951)
  • [7] Li P., Wu X., Hu X., Liang Q., Gao Y., A random decision tree ensemble for mining concept drifts from noisy data streams, Applied Artificial Intelligence, 7, pp. 680-710, (2010)
  • [8] Pratt K.B., Tschapek G., Visualizing concept drift, Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 03), pp. 735-740, (2003)
  • [9] Wu X., Li P., Hu X., Learning from concept drifting data streams with unlabeled data, Neurocomputing, pp. 145-155, (2012)
  • [10] Zhang P., Zhu X., Shi Y., Guo L., Wu X., Robust ensemble learning for mining noisy data streams, Decision Support Systems, 2, pp. 469-479, (2011)
12