DocumentCode :
2163207
Title :
Online learning with minority class resampling
Author :
Pekala, Michael J. ; Llorens, Ashley J.
Author_Institution :
Appl. Phys. Lab., Johns Hopkins Univ., Baltimore, MD, USA
fYear :
2011
fDate :
22-27 May 2011
Firstpage :
2248
Lastpage :
2251
Abstract :
This paper considers using online binary classification for target detection where the goal is to identify signals of interest within a sequence of received signals generated by a shifting background. In this setting, we assume there is significant class imbalance (100:1 or greater), the sequence of examples is arbitrarily long and the distribution of the majority (negative) class is slowly time-varying. This setting is typical in detection and classification problems in which time-varying effects are caused by some combination of shifting channel characteristics and interferers that enter and exit the scene. We show empirically that the addition of caching and minority class oversampling to online learners improves the g-means performance under these conditions by compensating for class imbalance.
Keywords :
learning (artificial intelligence); object detection; signal classification; g-means performance; interferers; minority class resampling; online binary classification; online learning; target detection; time-varying; Accuracy; Complexity theory; Kernel; Machine learning; Prediction algorithms; Sensitivity; Training; Support vector machine; class imbalance; classification; online learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Conference_Location :
Prague
ISSN :
1520-6149
Print_ISBN :
978-1-4577-0538-0
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2011.5946929
Filename :
5946929
Link To Document :
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