NDLI: Cleansing Noisy Data Streams

Content Provider	IEEE Xplore Digital Library
Author	Xingquan Zhu Peng Zhang Xindong Wu Dan He Chengqi Zhang Yong Shi
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Florida Atlantic Univ., Boca Raton, FL (Xingquan Zhu)
Abstract	In this paper, we identify a new research problem on cleansing noisy data streams which contain incorrectly labeled training examples. The objective is to accurately identify and remove mislabeled data, such that the prediction models built from the cleansed streams can be more accurate than the ones trained from the raw noisy streams. For this purpose, we first use bias-variance decomposition to derive a maximum variance margin (MVM) principle for stream data cleansing. Following this principle, we further propose a local and global filtering (LgF) framework to combine the strength of local noise filtering (within one single data chunk) and global noise filtering (across a number of adjacent data chunks) to identify erroneous data. Experimental results on six data streams (including two real-world data streams) demonstrate that LgF significantly outperforms simple methods in identifying noisy examples.
Starting Page	1139
Ending Page	1144
File Size	415227
Page Count	6
File Format	PDF
ISBN	9780769535029
ISSN	15504786
DOI	10.1109/ICDM.2008.45
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-15
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Filtering Voting Data mining Computer science Data engineering USA Councils Supervised learning Information technology Working environment noise data cleansing classification data streams
Content Type	Text
Resource Type	Article

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