Below you can find all the Imbalanced data sets available with imbalance ratio between 1.5 and 9. For each data set, it is shown its name and its number of instances, attributes (Real/Integer/Nominal valued) and imbalance ratio value.

The table allows to download each data set in KEEL format (inside a ZIP file). Additionally, it is possible to obtain the data set already partitioned, by means of a 5-folds cross validation procedure.

By clicking in the column headers, you can order the table by names (alphabetically), by the number of examples, attributes or IR. Clicking again will sort the rows in reverse order.

Below you can find the first block of the Imbalanced data sets available with imbalance ratio higher than 9. For each data set, it is shown its name and its number of instances, attributes (Real/Integer/Nominal valued) and imbalance ratio value.

The table allows to download each data set in KEEL format (inside a ZIP file). Additionally, it is possible to obtain the data set already partitioned, by means of a 5-folds cross validation procedure.

By clicking in the column headers, you can order the table by names (alphabetically), by the number of examples, attributes or IR. Clicking again will sort the rows in reverse order.

Below you can find the second block of the Imbalanced data sets available with imbalance ratio higher than 9. For each data set, it is shown its name and its number of instances, attributes (Real/Integer/Nominal valued) and imbalance ratio value.

The table allows to download each data set in KEEL format (inside a ZIP file). Additionally, it is possible to obtain the data set already partitioned, by means of a 5-folds cross validation procedure.

By clicking in the column headers, you can order the table by names (alphabetically), by the number of examples, attributes or IR. Clicking again will sort the rows in reverse order.

Below you can find the third block of the Imbalanced data sets available with imbalance ratio higher than 9. For each data set, it is shown its name and its number of instances, attributes (Real/Integer/Nominal valued) and imbalance ratio value.

The table allows to download each data set in KEEL format (inside a ZIP file). Additionally, it is possible to obtain the data set already partitioned, by means of a 5-folds cross validation procedure.

By clicking in the column headers, you can order the table by names (alphabetically), by the number of examples, attributes or IR. Clicking again will sort the rows in reverse order.

Below you can find all the Multi-class Imbalanced data sets available. For each data set, it is shown its name and its number of instances, attributes (Real/Integer/Nominal valued) and imbalance ratio value.

The table allows to download each data set in KEEL format (inside a ZIP file). Additionally, it is possible to obtain the data set already partitioned, by means of a 5-folds cross validation procedure.

By clicking in the column headers, you can order the table by names (alphabetically), by the number of examples, attributes or IR. Clicking again will sort the rows in reverse order.

Below you can find several synthetic Imbalanced data sets used in the above paper and whose examples are divided into 3 categories by the authors: safe, borderline and noisy examples.

   - Borderline examples are located in the area surrounding class boundaries, where the minority and majority classes overlap.
   - Safe examples are placed in relatively homogeneous areas with respect to the class label.
   - Noisy examples are individuals from one class occurring in safe areas of the other class.

For each data set, it is shown its name and its number of instances, attributes (Real/Integer/Nominal valued) and imbalance ratio value.

The table allows to download each data set in KEEL format (inside a ZIP file). Additionally, it is possible to obtain the data set already partitioned, by means of a 5-folds cross validation procedure.

By clicking in the column headers, you can order the table by names (alphabetically), by the number of examples, attributes or IR. Clicking again will sort the rows in reverse order.

This subsection contains a collection of some of the previous data sets already preprocessed by several oversampling techniques. For each technique, a ZIP file containing 5-folds cross validation partitions for each of the data sets of this page is provided. Moreover, a brief description and references about each method can be found below:

Imbalance ratio between 1.5 and 9

Imbalance ratio higher than 9 - Part I

Imbalance ratio higher than 9 - Part II

• SMOTE: The Synthetic Minority Over-sampling Technique (Chawla et al, 2002) is an oversampling technique of the minority class. It works by taking each minority class sample and introducing synthetic examples along the line segments joining any/all of the k minority class nearest neighbours.
• SMOTE+ENN: This method consists of the application of the Edited Nearest Neighbor rule (ENN, Wilson, 1972) as cleaning method over the data set obtained by the application of SMOTE. It was proposed by Batista et al, 2004, where the use of 3 neighbors for ENN is suggested.
• SMOTE+Tomek Links: This method consists of the application of Tomek Links (Tomek, 1976) as cleaning method over the data set obtained by the application of SMOTE. It was proposed by Batista et al, 2004.
• Bordeline: This methods only oversample or strengthen the borderline minority examples (Han et al, 2005). First, it finds out the borderline minority examples P; then, synthetic examples are generated from them and are added to the original training set. This method, for every minority examples (pi) calculate its m nearest neighbors from the whole training set. The number of majority examples among the m nearest neighbors is n. If all the m nearest neighbors are majority examples, pi is considered to be noise and is not operated in the following step. If m/2 <= n < m, namely the number of pi's majority nearest neighbors is larger than the number of its minority ones, pi is considered to be easily misclassified and put into a set called DANGER. If 0 <= n < m/2, pi is safe and does not need to participate in the following steps. The examples in the DANGER set are the borderline data of the minority class P. For each example in DANGER, we calculate its k nearest neighbors from P and we operate similarly to SMOTE.
• SafeLevels: This method (Bunkhumpornpat et al, 2009) computes for each positive instance its safe level before generating synthetic instances. Each synthetic instance is positioned closer to the largest safe level, so all synthetic instances are generated only in safe regions.
• SMOTE-RSB*: This method (Ramentol et al, 2011) first applies the SMOTE algorithm, and then, it only selects the minority synthetic examples that belong to the lower approximation using Rough Set Theory (Pawlak, 1982). This process is repeated until the training set is balanced.