Studies on arti cial neural network have been conducted for a long time, and its contribution has been shown in many elds. However, the application of neural networks in the real world domain is still a challenge, since nature does not always provide the required satisfactory conditions. One example is the class size imbalanced condition in which one class is heavily under-represented compared ...

Learning from imbalanced data is a problem which arises in many real-world scenarios, so does the need to build classifiers able to predict more than one class label simultaneously (multilabel classification). Dealing with imbalance by means of resampling methods is an approach that has been deeply studied lately, primarily in the context of traditional (non-multilabel) classification. In this ...

A characteristics of imbalanced points is their localities—an imbalanced point may be contiguous to some other imbalanced points in terms of 8-connectivity. A two-layer scheme was recently proposed for matching imbalanced points based on localities, where the first layer aims to build locality correspondence, and the second layer aims to build point correspondence within corresponding localitie...

In practice, pattern recognition applications often suffer from imbalanced data distributions between classes, which may vary during operations w.r.t. the design data. Two-class classification systems designed using imbalanced data tend to recognize the majority (negative) class better, while the class of interest (positive class) often has the smaller number of samples. Several data-level tech...

Graph contrastive learning (GCL) has attracted a surge of attention due to its superior performance for node/graph representations without labels. However, in practice, the underlying class distribution unlabeled nodes given graph is usually imbalanced. This highly imbalanced inevitably deteriorates quality learned node GCL. Indeed, we empirically find that most state-of-the-art GCL methods can...

Recommendation is the task of improving customer experience through personalized recommendation based on users’ past feedback. In this paper, we investigate the most common scenario: the user-item (U-I) matrix of implicit feedback (e.g. clicks, views, purchases). Even though many recommendation approaches are designed based on implicit feedback, they attempt to project the U-I matrix into a low...

Objective: The traditional classifiers are ineffective in classifying the imbalanced datasets. Most popular approach resolving this problem is through data re-sampling. A hybrid resampling method proposed paper that reduces misclassification all classes. Method: employs Leader algorithm for under sampling and SMOTE oversampling. It generates desired number of samples both classes based on overc...

Abstract Convolutional neural networks (CNNs) have achieved impressive results on imbalanced image data, but they still difficulty generalizing to minority classes and their decisions are difficult interpret. These problems related because the method by which CNNs generalize classes, requires improvement, is wrapped in a black-box. To demystify CNN we focus latent features. Although embed patte...