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IJCNN
2006
IEEE
13 years 11 months ago
Data Fusion for Outlier Detection through Pseudo-ROC Curves and Rank Distributions
— This paper proposes a novel method of fusing models for classification of unbalanced data. The unbalanced data contains a majority of healthy (negative) instances, and a minor...
Paul F. Evangelista, Mark J. Embrechts, Boleslaw K...
IJSNET
2010
122views more  IJSNET 2010»
13 years 4 months ago
Ensuring high sensor data quality through use of online outlier detection techniques
: Data collected by Wireless Sensor Networks (WSNs) are inherently unreliable. Therefore, to ensure high data quality, secure monitoring, and reliable detection of interesting and ...
Yang Zhang, Nirvana Meratnia, Paul J. M. Havinga
CIDM
2007
IEEE
14 years 4 days ago
Incremental Local Outlier Detection for Data Streams
Outlier detection has recently become an important problem in many industrial and financial applications. This problem is further complicated by the fact that in many cases, outlie...
Dragoljub Pokrajac, Aleksandar Lazarevic, Longin J...
KDD
2008
ACM
234views Data Mining» more  KDD 2008»
14 years 6 months ago
Angle-based outlier detection in high-dimensional data
Detecting outliers in a large set of data objects is a major data mining task aiming at finding different mechanisms responsible for different groups of objects in a data set. All...
Hans-Peter Kriegel, Matthias Schubert, Arthur Zime...
MLDM
2007
Springer
13 years 12 months ago
Outlier Detection with Kernel Density Functions
Abstract. Outlier detection has recently become an important problem in many industrial and financial applications. In this paper, a novel unsupervised algorithm for outlier detec...
Longin Jan Latecki, Aleksandar Lazarevic, Dragolju...