Experimental Study on Class Imbalance Problem Using an Oil Spill Training Data Set
Xi Qin Ouyang *
Department of Mathematics, Ganzhou Teachers College, Ganzhou 341000, Jiangxi, China.
Yuan Ping Chen
Department of Foundation, Jiangxi Environmental Engineering Vocational College, Ganzhou 341000, Jiangxi, China.
Bing Hui Wei
Department of Computer Science, Jiangxi University of Science and Technology, Ganzhou 341000, Jiangxi, China.
*Author to whom correspondence should be addressed.
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.
Keywords: Imbalanced problem, oil spill detection, data set.