Graph neural networks (GNNs) have achieved great success in node classification tasks. However, existing GNNs naturally bias towards the majority classes with more labelled data and ignore those minority relatively few ones. The traditional techniques often resort over-sampling methods, but they may cause overfitting problem. More recently, some works propose to synthesize additional nodes for ...

Many machine learning problem domains, such as the detection of fraud, spam, outliers, and anomalies, tend to involve inherently imbalanced class distributions samples. However, most classification algorithms assume equivalent sample sizes for each class. Therefore, datasets pose a significant challenge in prediction modeling. Herein, we propose density-based random forest algorithm (DBRF) impr...

The imbalanced data classification remains a vital problem. key is to find such methods that classify both the minority and majority class correctly. paper presents classifier ensemble for classifying binary, non-stationary streams where Hellinger Distance used prune ensemble. includes an experimental evaluation of method based on conducted experiments. first one checks impact base type quality...

Classification of imbalanced data is a well explored issue in the mining and machine learning community where one class representation overwhelmed by other classes. The Imbalanced distribution natural occurrence real world datasets, so needed to be dealt with carefully get important insights. In case imbalance sets, traditional classifiers have sacrifice their performances, therefore lead miscl...

In the field of classification problems, we often encounter classes with a very different percentage of patterns between them, classes with a high pattern percentage and classes with a low pattern percentage. These problems receive the name of “classification problemswith imbalanced data-sets”. In this paperwe study the behaviour of fuzzy rule based classification systems in the framework of im...

Data in many biological problems are often compounded by imbalanced class distribution. That is, the positive examples may largely outnumbered by the negative examples. Many classification algorithms such as support vector machine (SVM) are sensitive to data with imbalanced class distribution, and result in a suboptimal classification. It is desirable to compensate the imbalance effect in model...