Below you can find all the Standard Classification data sets available. For each data set, it is shown its name and its number of instances, attributes (the table details the number of Real/Integer/Nominal attributes in the data) and classes (number of possible values of the output variable). In addition, the table shows if the corresponding data set has missing values or not (for data sets with missing values the table shows the number of instances without missing values, and the total number of instances between brackets).

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 10-folds / 5-folds stratified cross validation (SCV) procedure. The partitions using a 10-folds / 5-folds distribution optimally balanced stratified cross-validation (DOB-SCV) are also available (except for those datasets with a very high number of examples, since this partitioning scheme requires a considerable computation). The latter validation procedure was proposed in:

J.G. Moreno-Torres, J.A. Sáez, F. Herrera, Study on the impact of partition-induced dataset shift on k-fold cross-validation, IEEE Transactions on Neural Networks and Learning Systems 23 (8) (2012) 1304-1313.

For data sets with missing values, only the cleaned version (where instances with missing values are not included) is provided. A complete version including instances with missing values can be found in the description page of each data set or in the missing values section of KEEL-dataset. Finally, we provide a header file to give additional information about each data set and its attributes.

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