Publikationsserver der Universitätsbibliothek Marburg

Titel: Label Ranking with Probabilistic Models
Autor: Cheng, Weiwei
Weitere Beteiligte: Hüllermeier, Eyke (Prof.)
Veröffentlicht: 2012
URI: https://archiv.ub.uni-marburg.de/diss/z2012/0493
DOI: https://doi.org/10.17192/z2012.0493
URN: urn:nbn:de:hebis:04-z2012-04934
DDC: 004 Informatik
Titel(trans.): Label Ranking mit probabilistischen Modellen
Publikationsdatum: 2012-07-11
Lizenz: https://rightsstatements.org/vocab/InC-NC/1.0/

Dokument

Schlagwörter:
Artificial Intelligence: AI, Maschinelles Lernen, Preference learning, Label ranking, Probabilistic models

Summary:
Diese Arbeit konzentriert sich auf eine spezielle Prognoseform, das sogenannte Label Ranking. Auf den Punkt gebracht, kann Label Ranking als eine Erweiterung des herkömmlichen Klassifizierungproblems betrachtet werden. Bei einer Anfrage (z. B. durch einen Kunden) und einem vordefinierten Set von Kandidaten Labels (zB AUDI, BMW, VW), wird ein einzelnes Label (zB BMW) zur Vorhersage in der Klassifizierung benötigt, während ein komplettes Ranking aller Label (zB BMW> VW> Audi) für das Label Ranking erforderlich ist. Da Vorhersagen dieser Art, bei vielen Problemen der realen Welt nützlich sind, können Label Ranking-Methoden in mehreren Anwendungen, darunter Information Retrieval, Kundenwunsch Lernen und E-Commerce eingesetzt werden. Die vorliegende Arbeit stellt eine Auswahl an Methoden für Label-Ranking vor, die Maschinelles Lernen mit statistischen Bewertungsmodellen kombiniert. Wir konzentrieren wir uns auf zwei statistische Ranking-Modelle, das Mallows- und das Plackett-Luce-Modell und zwei Techniken des maschinellen Lernens, das Beispielbasierte Lernen und das Verallgemeinernde Lineare Modell.

Zusammenfassung:
Machine learning methods induce models from observed data in order to make predictions for future events. This thesis focuses on a specific prediction task called label ranking, a problem that has recently attracted much attention in the machine learning community. In a nutshell, label ranking can be considered as an extension of the conventional classification problem. Given a query instance (e.g., a customer) and a predefined set of candidate labels (e.g., AUDI, BMW, VW), a single label is required as a prediction in classification (e.g. BMW), while a complete ranking of all labels is required in label ranking (e.g., BMW > VW > AUDI). Since predictions of that kind are useful in many real-world problems, label ranking methods can be applied in multiple domains, including information retrieval, customer preference learning, and online e-commerce. This thesis provides a set of methods for label ranking by combining the statistical ranking models and machine learning techniques. The connection of these two fields has not been studied for label ranking before. More precisely, we concentrate on two statistical ranking models, the Mallows and the Plackett-Luce model and two machine learning techniques, instance-based learning and generalized linear model.

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