Experimental Dataset for Imbalanced Classification: Application of Relabeling & Ranking Algorithm

The datasets in the study "Relabeling & Raking Algorithm for Imbalanced Classification" were sourced from several public repositories, including 1) Knowledge Extraction based on Evolutionary Learning data repository (J. Alcal´a-Fdez and A. Fernandez and J. Luengo and J. Derrac and S. Garc´ıa and L. S´anchez and F. Herrera, 16 2011), 2) UCI machine learning repository (Dua and Graff, 2017), 3) HDDT collection (Cieslak et al., 2012) and 4) previous studies (Radivojac et al., 2004; Kubat et al., 1998; WOODS et al., 1993). These datasets are particularly notable for their imbalanced nature and are widely recognized in academic literature for this feature.

Two main criteria were used to select these datasets:

Large-Scale Focus: Preference was given to large-scale datasets, a category often overlooked in previous studies. This selection includes datasets with more than 1,000 instances, with 10 of the 16 real-world datasets exceeding this threshold and four having over 10,000 instances.

High Imbalance Ratio (IR): The primary focus was on highly imbalanced datasets, specifically those with an IR greater than 9.

The datasets were categorized based on the types of feature variables they contain:
Continuous datasets: All feature variables are continuous.
Categorical datasets: All feature variables are categorical.
Mixed datasets: A combination of continuous and categorical feature variables.