Understanding the apparent superiority of over-sampling through an analysis of local information for class-imbalanced dataClass imbalanceSample typesResamplingLocal neighborhoodData plays a key role in the desig
Missing labels in multi-label datasets are a common problem, especially for minority classes, which are more likely to occur. This limitation hinders the p
With the rapid expansion of data, the problem of data imbalance has become increasingly prominent in the fields of medical treatment, finance, network, etc. And it is typically solved using the oversampling method. However, most existing oversampling met
2025, Neural Networks Show abstract A comprehensive survey of transfer dictionary learning 2025, Neurocomputing Show abstract Deep generative approaches for oversampling in imbalanced data classification problems: A comprehensive review and comparative analysis 2025, Applied Soft Computing Show abstractView...
For Fashion-MNIST, SMOTE+CN is performed in the feature space learned by baseline CN. Oversample+CN (minority class images randomly sampled with replacement), Augment+CN (data augmentation to create new images and balance the training set), and DOS are also considered during comparison, while ...
Since the advent of Graph Neural Networks (GNNs), they have been widely applied in the analysis and processing of graph data, especially demonstrating outs
Analog Devices has introduced two new CMOS high-speed 16-bit oversampling A/D converters for handling wideband signals with wide dynamic range in applications where low power, small footprint, and low-cost monolithic solutions are essential.The...
Analysis of SPT data including mixed populations In order to evaluate how our approach could discriminate mixed subpopulations in a sample, as is the case in cells, we constituted mixtures of bead trajectories within various glycerol contents, from the previous data. We chose to use the histogram...
However, when handling multiclass imbalanced data, oversampling techniques face new challenges not present in two-class scenario. First, multiclass imbalanced data might have multimajority. The minority class instances are easier to be ignored by the learning algorithm as a result of the existence ...
Whereas, in unsupervised learning, input data does not have any dependent target class, and the learning method is used to explore hidden patterns in data [2]. There are many different classification algorithms exist in the literature, such as Decision Tree (DT), Support Vector Machines (SVM)...