NDLI: A Dynamic Sampling Framework for Multi-class Imbalanced Data

Content Provider	IEEE Xplore Digital Library
Author	Debowski, B. Areibi, S. Grewal, G. Tempelman, J.
Copyright Year	2012
Abstract	In this paper we present a Dynamic Sampling Framework for use with multi-class imbalanced data containing any number of classes. The framework makes use of existing sampling techniques such as RUS, ROS, and SMOTE and ties the classification algorithm into the sampling process in a wrapper like manner. In doing so the framework is able to search for a desirably sampled training set, thus eliminating the need to specify a target distribution and automatically tuning the training set distribution to the classification algorithm's learning preferences. This is important when re-sampling multi-class data where manually searching for an appropriate target distribution would be a daunting task. We test both our Dynamic Sampling approach and traditional Static Sampling using RUS, ROS, SMOTE, ROS+RUS, and SMOTE+RUS with several classification algorithms on a four class, highly imbalanced data set. We compare the results of Static Sampling and Dynamic Sampling and find that overall both techniques are able to raise Recall for the highest minority classes, but Dynamic Sampling is also able to maintain or raise Recall for the majority classes. Also, Dynamic Sampling is overall more robust and resilient, and is better able to sustain classifier Accuracy and to raise G-Mean and Minimum F-Measures.
Starting Page	113
Ending Page	118
File Size	306774
Page Count	6
File Format	PDF
ISBN	9781467346511
DOI	10.1109/ICMLA.2012.144
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Algorithm design and analysis Accuracy Heuristic algorithms Artificial neural networks Educational institutions Multi-class Niobium Dynamic Sampling Imbalanced Data
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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