Preference Learning
Inhaltsverzeichnis
Reference
Johannes Fürnkranz, Eyke Hüllermeier (eds.): Preference Learning. Springer Berlin Heidelberg, 2011. ISBN: 978-3-642-14124-9 (Print) 978-3-642-14125-6 (Online)
DOI
http://dx.doi.org/10.1007/978-3-642-14125-6
Abstract
The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in recent years. Representing and processing knowledge in terms of preferences is appealing as it allows one to specify desires in a declarative way, to combine qualitative and quantitative modes of reasoning, and to deal with inconsistencies and exceptions in a flexible manner. Preference learning is concerned with the acquisition of preference models from data – it involves learning from observations that reveal information about the preferences of an individual or a class of individuals, and building models that generalize beyond such training data. This is the first book dedicated to this topic, and the treatment is comprehensive. The editors first offer a thorough introduction, including a systematic categorization according to learning task and learning technique, along with a unified notation. The remainder of the book is organized into parts that follow the developed framework, complementing survey articles with in-depth treatises of current research topics in this area. The book will be of interest to researchers and practitioners in artificial intelligence, in particular machine learning and data mining, and in fields such as multicriteria decision-making and operations research.
Extended Abstract
Reviews
"The book looks at three major types of preference learning: label ranking, instance ranking, and object ranking. … chapters contain case studies and actual experiments to illustrate the claims made within. … this is a useful book in an emerging and important area, and hence would be of interest to machine learning researchers. The book is quite readable to that audience, despite a heavy emphasis on formal treatment." M. Sasikumar, ACM Computing Reviews, September, 2011
Bibtex
@book{
year={2011},
isbn={978-3-642-14124-9 (Print), 978-3-642-14125-6 (Online)},
booktitle={Preference Learning},
editor={Fürnkranz, Johannes and Hüllermeier, Eyke},
doi={10.1007/978-3-642-14125-6},
url={http://dx.doi.org/10.1007/978-3-642-14125-6, http://de.evo-art.org/index.php?title=Preference_Learning },
publisher={Springer Berlin Heidelberg},
language={English}
}
Table of contents (20 chapters)
- Johannes Fürnkranz, Eyke Hüllermeier: Preference Learning: An Introduction. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 1-17. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_1
- Fabio Aiolli, Alessandro Sperduti: A Preference Optimization Based Unifying Framework for Supervised Learning Problems. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 19-42. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_2
Label Ranking
- Shankar Vembu, Thomas Gärtner: Label Ranking Algorithms: A Survey. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 45-64. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_3
- Johannes Fürnkranz, Eyke Hüllermeier: Preference Learning and Ranking by Pairwise Comparison. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 65-82. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_4
- Philip L. H. Yu, Wai Ming Wan, Paul H. Lee: Decision Tree Modeling for Ranking Data. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 83-106. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_5
- Evgeni Tsivtsivadze, Tapio Pahikkala, Jorma Boberg, Tapio Salakoski, Tom Heskes: Co-Regularized Least-Squares for Label Ranking. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 107-123. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_6
Instance Ranking
- Willem Waegeman, Bernard De Baets: A Survey on ROC-based Ordinal Regression. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 127-154. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_7
- Jianping Zhang, Jerzy W. Bala, Ali Hadjarian, Brent Han: Ranking Cases with Classification Rules. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 155-177. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_8
Object Ranking
- Toshihiro Kamishima, Hideto Kazawa, Shotaro Akaho: A Survey and Empirical Comparison of Object Ranking Methods. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 181-201. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_9
- Toshihiro Kamishima, Shotaro Akaho: Dimension Reduction for Object Ranking. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 203-215. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_10
- Krzysztof Dembczyński, Wojciech Kotłowski, Roman Słowiński, Marcin Szeląg: Learning of Rule Ensembles for Multiple Attribute Ranking Problems. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 217-247. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_11
Preferences in Multi-Attribute Domains
- Fusun Yaman, Thomas J. Walsh, Michael L. Littman, Marie desJardins: Learning Lexicographic Preference Models. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 251-272. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_12
- Yann Chevaleyre, Frédéric Koriche, Jérôme Lang, Jérôme Mengin, Bruno Zanuttini: Learning Ordinal Preferences on Multiattribute Domains: The Case of CP-nets. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 273-296. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_13
- Joachim Giesen, Klaus Mueller, Bilyana Taneva, Peter Zolliker : Choice-Based Conjoint Analysis: Classification vs. Discrete Choice Models. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 297-315. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_14
- Vicenç Torra: Learning Aggregation Operators for Preference Modeling. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 317-333. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_15
Preferences in Information Retrieval
Evaluating Search Engine Relevance with Click-Based Metrics
Radlinski, Filip (et al.)
Seiten 337-361
Learning SVM Ranking Functions from User Feedback Using Document Metadata and Active Learning in the Biomedical Domain
Arens, Robert
Seiten 363-383
Learning Preference Models in Recommender Systems
Gemmis, Marco de (et al.)
Seiten 387-407
Collaborative Preference Learning
Karatzoglou, Alexandros (et al.)
Seiten 409-427
Discerning Relevant Model Features in a Content-based Collaborative Recommender System
Bellogín, Alejandro (et al.)
Seiten 429-455
