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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.
This SourceForge project provides an open source preference learning toolbox (PLT) that supports the key data modelling phases incorporating various popular data preprocessing, feature selection and preference learning methods.
Information about how to use and modify the tool is available on the wiki:
https://github.com/TAPeri/pl-toolbox/wiki
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)
Features
- Dataset Preprocessing
- Automatic Feature Selection (nBest, SFS)
- Preference Learning Algorithms (Ranking SVM, ANN-BP, EANN)
- Experiment Reporting and Model Storage
Project Samples
