A Study on the Improvement of Image Classification Performance in the Defense Field through Cost-Sensitive Learning of Imbalanced Data
Miae Jeong, Jungmok Ma · Journal of the Korea Institute of Military Science and Technology · 2021
With the development of deep learning technology, researchers and technicians keep attempting to apply deep learning in various industrial and academic fields, including the defense.Most of these attempts assume that the data are balanced.In reality, since lots of the data are imbalanced, the classifier is not properly built and the model's performance can be low.Therefore, this study proposes cost-sensitive learning as a solution to the imbalance data problem of image classification in the defense field.In the proposed model, cost-sensitive learning is a method of giving a high weight on the cost function of a minority class.The results of cost-sensitive based model shows the test F1-score is higher when cost-sensitive learning is applied than general learning's through 160 experiments using submarine/non-submarine dataset and warship/non-warship dataset.Furthermore, statistical tests are conducted and the results are shown significantly.