Proefschrift

Pattern mining for label ranking

Promotor: J.N. Kok, Co-promotor: C.M. Soares, A.J. Knobbe

Auteur

C.F. Pinho Rebelo de Sá

Datum

16 december 2016

Links

Thesis in Leiden Repository

Preferences have always been present in many tasks in our daily lives. Buying the right car, choosing a suitable house or even deciding on the food to eat, are trivial examples of decisions that reveal information, explicitly or implicitly, about our preferences. The recent trend of collecting increasing amounts of data is also true for preference data. Extracting and modeling preferences can provide us with invaluable information about the choices of groups or individuals. In areas like e-commerce, which typically deal with decisions from thousands of users, the acquisition of preferences can be a difficult task. For these reasons, artificial intelligence (in particular, machine learning) methods have been increasingly important to the discovery and automatic learning of models about preferences. In this Ph.D. project, several approaches were analyzed and proposed to deal with the LR problem. Most of which has focused on pattern mining methods.

• association rules
• discretization
• label ranking
• patter mining
• preference learning