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Research Article Open access CC BY 4.0

Experimental Study on Class Imbalance Problem Using an Oil Spill Training Data Set

Xi Qin Ouyang, Yuan Ping Chen, Bing Hui Wei

Journal of Advances in Mathematics and Computer Science · pp. 1–9 · Published 13 Apr 2017

10.9734/BJMCS/2017/32860

Abstract

There is a paucity of research on one of the key issues in oil spill detection: the imbalanced training set learning problem. This paper performs experiments to show the influence of the imbalanced learning problem (ILP) on oil spill detection and devises a novel framework to tackle this problem. Experimental results show that an imbalanced training set degenerate the performance of oil spill detection, and our proposed framework achieves a better performance based on F-measure.

Imbalanced problem oil spill detection data set.

References (10)

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  8. 8 Oil spill detection using marine SAR images [DOI]
  9. 9 A learning method for the class imbalance problem with medical data sets [DOI]
  10. 10 An Effective Under-Sampling Method for Class Imbalance Data Problem

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