Preference learning (PL) is a core area of machine learning that handles datasets with ordinal relations. As the number of generated data of ordinal nature such as ranks and subjective ratings is increasing, the importance and role of the PL field becomes central within machine learning research and practice.
The Preference Learning Toolbox (PLT) is an open source software application and package which supports the key data modelling phases incorporating various popular data pre-processing, feature selection and preference learning methods.
Screenshot of the Preference Learning Toolbox.
PLT was developed at the Institute of Digital Games at the University of Malta with the support of Maltco Lotteries.
The latest version of PLT was developed in Python. An older version developed in Java is also available. PLT can be easily used via its graphical user interface (GUI). Information about how to use the tool can be found under the How To Use section.
The tool is free for scientific use. If you use PLT in your scientific work, please cite as:
Farrugia, Vincent E., Héctor P. Martínez, and Georgios N. Yannakakis.
"The Preference Learning Toolbox." arXiv preprint arXiv:1506.01709 (2015)
The Preference Learning Toolbox is shared under the GNU General Public License (version 3). A copy of this license may be downloaded from the Download section.
PLT Features:
- Dataset Pre-processing
- Automatic Feature Selection (SFS)
- Preference Learning Algorithms (RankSVM, ANN-Backpropagation)
- Experiment Reporting and Model Storage