Experimental Study on Class Imbalance Problem Using an Oil Spill Training Data Set
Journal Title: Journal of Advances in Mathematics and Computer Science - Year 2017, Vol 21, Issue 5
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.
Authors and Affiliations
Xi Qin Ouyang, Yuan Ping Chen, Bing Hui Wei
Keywords
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- EP ID EP321918
- DOI 10.9734/BJMCS/2017/32860
- Views 93
- Downloads 0
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