Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data

• Published in: IEEE transaction on neural networks and learning systems Vol. 29; no. 8; pp. 3573 - 3587
• Main Authors: Khan, Salman H., Hayat, Munawar, Bennamoun, Mohammed, Sohel, Ferdous A., Togneri, Roberto
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Australia; Classification; Classifiers; Computer applications; Computer vision; Convolutional neural networks (CNNs); cost-sensitive (CoSen) learning; data imbalance; Data sampling; Image classification; loss functions; Neural networks; Object recognition; Representations; Tag clouds; Testing; Training; Training data

Class imbalance is a common problem in the case of real-world object detection and classification tasks. Data of some classes are abundant, making them an overrepresented majority, and data of other classes are scarce, making them an underrepresented minority. This imbalance makes it challenging for...