The purpose of this study is to examine existing deep learning techniques for addressing
class imbalanced data. Effective classification with imbalanced data is an important area of …

Deep image classifiers often perform poorly when training data are heavily class-
imbalanced. In this work, we propose a new regularization technique, Remix, that relaxes …

In this study, we systematically investigate the impact of class imbalance on classification
performance of convolutional neural networks (CNNs) and compare frequently used …

Class imbalance is an active research area in the machine learning community. However,
existing and recent literature showed that class overlap had a higher negative impact on the …

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 …

Class imbalance is a long-standing problem relevant to a number of real-world applications
of deep learning. Oversampling techniques, which are effective for handling class imbalance …

Network traffic classification has become an important part of network management, which is
beneficial for achieving intelligent network operation and maintenance, enhancing the …

Most of the traditional pattern classifiers assume their input data to be well-behaved in terms
of similar underlying class distributions, balanced size of classes, the presence of a full set of …

Broad learning system (BLS) is a novel and efficient model, which facilitates representation
learning and classification by concatenating feature nodes and enhancement nodes. In spite …

Intrusion detection (ID) on the cloud environment has received paramount interest over the
last few years. Among the latest approaches, machine learning-based ID methods allow us …