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

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.