A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios

Class imbalance and class overlap are two of the major problems in data mining and machine learning. Several studies have shown that these data complexities may affect the performance or behavior of artificial neural networks. Strategies proposed to face with both challenges have been separately applied. In this paper, we introduce a hybrid method for handling both class imbalance and class overlap simultaneously in multi-class learning problems. Experimental results on five remote sensing data show that the combined approach is a promising method.

► Class overlap and class imbalance have been widely studied and treated separately. ► Rarely, class overlap and class imbalance are deal at the same time. ► New hybrid method for handling both class imbalance and class overlap simultaneously. ► Hybrid method is successful for dealing with class imbalance and class overlapping.