Class for extracting the main descriptive characteristics of a dataset based on WEKA's simplest classifier, ZeroR.
When used as a classification algorithm in the WEKA's Experimenter (located in the "rules" group), it returns the descriptive features (number of classes, number of attributes...) of a set of datasets as if they were metrics (Comparison field) used to evaluate the goodness of the classifier (like Percent_correct, Area_under_ROC, Elapsed_Time_training...).
For proper results configuration (Setup tab of the Experimenter), it's recommended to set the 'Experiment Type' to "Train/Test Percentage Split (order preserved)" with 100% 'Train Percentage'. This ensures measures like Number_of_training_instances or NumMissingValuesDataset aren't affected by Train/Test data splits of the default 'Cross-validation' option.
To obtain research-ready results, specify 'CSV file' as 'Results Destination' and provide a filename. After running the experiment, the generated CSV can be opened in spreadsheet software, displaying datasets in rows and their complete features (plus ZeroR metrics) in columns - similar to the dataset description tables commonly found in machine learning publications.
List of extracted characteristics (all starting with “measure” due to WEKA naming convention):
- NumAttributes: Number of attributes of the dataset (without class)
- NumNumericAttributes: Number of numeric attributes of the dataset (without class)
- NumNominalAttributes: Number of nominal attributes of the dataset (without class)
- MissingValues: Whether there are missing values in the dataset (1.0) or not (0.0) (without class)
- NumAttsMissingValues: Number of attributes with missing values (without class)
- NumMissingValuesDataset: Number of examples with missing values in the dataset (without class)
- PercentMissingValuesDataset: Percentage of examples with missing values in the dataset (without class)
- NumClasses: Number of classes in the dataset
- EmptyClass: Whether there are any empty classes (1.0) or not (0.0)
- NumFirstClass: Number of examples of the first class (considered by WEKA as positive by default)
- MinClassIndex: Index of minority class (discarding empty classes)
- NumMinClass: Number of examples of minority class (discarding empty classes)
- PercentMinClass: Percentage of examples of minority class (discarding empty classes)
- NumMajClass: Number of examples of majority class
- PercentMajClass: Percentage of examples of majority class
- ImbalancedRatio: Imbalanced Ratio (IR)
This class was used in the following paper where an extensive experimentation was carried out with 96 different datasets:
Jesús M. Pérez and Olatz Arbelaitz. "Multi-Criteria Node Selection in Direct PCTBagging: Balancing Interpretability and Accuracy with Bootstrap Sampling and Unrestricted Pruning". Information Sciences (2025), submitted. doi:10.1016/j.ins.2025.XX.XXX
The Weka package containing the DatasetCharacteristicsExtractor tool to be installed from Weka's package manager, including compiled code, source code, javadocs and package description files, can be found here:
The source code of the class that implements the DatasetCharacteristicsExtractor tool (on stable-3-8-6 version of Weka) can be found in:
In order to complete the whole source code of the implementation, download the Weka source code from https://waikato.github.io/weka-wiki/downloading_weka/.
This binary can be added to the classpath of a WEKA execution and the tool will be available as a classifier under the "rules" group.