Reads a source that is in KEEL (Knowledge Extraction based on Evolutionary Learning) format.
This class was used in the following paper where an extensive experimentation was carried out with 96 different datasets downloaded from the KEEL repository:
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 KEELLoader package to be installed from Weka's package manager, including compiled code, source code, javadocs and package description files, can be found here:
With the installation of this package, the KEEL format can be used to load a dataset into the Weka Explorer application. To be able to use it also in the Experimenter, see the "Source code" section.
The source code of the class that implements the KEELLoader converter (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/.
In order to be able to use the KEEL format also in the Weka Experimenter application, you will have to add to the file "ConverterResources.java" (in main/java/weka/core/converters/) the line of code associated with the KEELLoader class in the following figure:
A patch file with the lines of code added to the original source code can also be found here.
The executable file in Weka is a .jar file. The file with the current KEELLoader converter included in the stable-3-8-6 version of Weka (available in both Explorer and Experimenter) can be found in:
To run Weka type:
java -Xmx1000M -jar Weka-KEELLoader.jar
(see https://waikato.github.io/weka-wiki/downloading_weka/ for more information)