14 datasets found

The Hellinger Distance Weighted Ensemble (hdwe) method for batch learning and the binary classification data stream with occurring concept drifts and the imbalance among classes.

The ImbR data set consists of regression data set with 1000 cases and two features, X1 (numeric) and X2 (numeric). The target variable, denoted as Tgt, has a continuous...

The ImbC data set consists of multiclass classification data set with 1000 cases and two features, X1 (numeric) and X2 (nominal). The target variable, denoted as Class, has two...

Highly imbalanced datasets are ubiquitous in medical image classification problems. In such problems, it is often the case that rare classes associated to less prevalent...

This research tested the following well known strategies to deal with binary imbalanced data on 82 different real life data sets

The proposed model is applied to two publicly available datasets, the first is the Cancer Genome Atlas (TCGA) Breast Invasive Carcinoma (BRCA) dataset, which contains DNA...

Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients.

ImVerde: Vertex-diminished random walk for learning network representation from imbalanced data

The dataset used in this paper is a real-world dataset for imbalanced regression problems.

The dataset used in this paper is a real-world dataset for imbalanced regression problems.

The dataset used in this paper is a real-world dataset for imbalanced regression problems.

The dataset used in this paper is a real-world dataset for imbalanced regression problems.

The Abalone dataset has 4177 observation 8 covariates. Sex is a nominal variable with 3 categories: Male, Female and Infant. Length (mm) is the longest shell measurement,...

The dataset used in this paper is a real-world dataset for imbalanced regression problems.