=Paper=
{{Paper
|id=Vol-1917/paper16
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1917/paper16.pdf
|volume=Vol-1917
|authors=Eyke Hüllermeier
}}
==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1917/paper16.pdf</pdf>
<pre>
                       jPL: A Java-based Software Framework for
                                  Preference Learning

                                  Pritha Gupta, Alexander Hetzer, Tanja Tornede,
                          Sebastian Gottschalk, Andreas Kornelsen, Sebastian Osterbrink,
                                   Karlson Pfannschmidt, and Eyke Hüllermeier

                                                  Intelligent Systems Group
                                                    Paderborn University

                     Preference learning (PL) is an emerging subfield of machine learning, which
                 deals with the induction of preference models from observed preference informa-
                 tion [3]. Such models are typically used for prediction purposes, for example to
                 predict context-dependent preferences of individuals on various choice alterna-
                 tives. Depending on the representation of preferences, individuals, alternatives,
                 and contexts, a large variety of preference models and problems are conceivable.
                     We developed a software framework offering tools and algorithms for solving
                 preference learning problems.1 While software frameworks for core machine learn-
                 ing problems such as classification abound, we are not aware of any comprehensive
                 library of tools for preference learning. In fact, existing libraries are essentially
                 restricted to one or two types of PL problems (e.g. [2], [6], [5], [4], [1]).
                     Our framework, called jPL, is implemented in Java. It is based on a unified data
                 format, the Generic Preference Representation Format (GPRF), which is suitable
                 for modeling data related to different kinds of preference learning problems.
                 This also includes a dataset transformer, which converts data from several
                 existing formats to GPRF. As problem classes, the framework currently supports
                 collaborative filtering, instance ranking, label ranking, multilabel classifcation,
                 object ranking, ordinal classification, and rank aggregation out of the box, with at
                 least two algorithms being implemented for each problem. It provides a convenient
                 command line interface as well as an API, both allowing one to configure the
                 system using json files. The whole framework was developed in a quite generic
                 way, so as to allow other problems and algorithms to be added easily.
                     Our framework also supports the evaluation and comparison of different meth-
                 ods in terms of standard validation techniques, and includes a set of commonly
                 used loss functions. Just like the framework as a whole, the evaluation component
                 is easily extensible by new evaluation techniques and loss functions.


                 References

                 1. V Dang. The lemur project-wiki-ranklib. lemur project.
                 2. Vincent E Farrugia, Héctor P Martı́nez, and Georgios N Yannakakis. The preference
                    learning toolbox. arXiv preprint arXiv:1506.01709, 2015.
                 1
                     https://github.com/intelligent-systems-group/jpl-framework




Copyright © 2017 by the paper’s authors. Copying permitted only for private and academic purposes.
In: M. Leyer (Ed.): Proceedings of the LWDA 2017 Workshops: KDML, FGWM, IR, and FGDB.
Rostock, Germany, 11.-13. September 2017, published at http://ceur-ws.org
3. Johannes Fürnkranz and Eyke Hüllermeier. Preference learning: An introduction.
   In Preference learning, pages 1–17. Springer, 2010.
4. Nicholas Mattei and Toby Walsh. Preflib: A library for preferences http://www.
   preflib. org. In International Conference on Algorithmic DecisionTheory, pages
   259–270. Springer, 2013.
5. Jesse Read, Peter Reutemann, Bernhard Pfahringer, and Geoff Holmes. Meka:
   a multi-label/multi-target extension to weka. The Journal of Machine Learning
   Research, 17(1):667–671, 2016.
6. Grigorios Tsoumakas, Eleftherios Spyromitros-Xioufis, Jozef Vilcek, and Ioannis
   Vlahavas. Mulan: A java library for multi-label learning. Journal of Machine
   Learning Research, 12(Jul):2411–2414, 2011.

</pre>