Label Ranking (LR) problems are becoming increasingly important in Machine Learning.
While there has been a significant amount of work on the development of learning …

Preference learning is the branch of machine learning in charge of inducing preference
models from data. In this paper we focus on the task known as label ranking problem, whose …

Abstract The problem of Label Ranking is receiving increasing attention from several
research communities. The algorithms that have been developed/adapted to treat rankings …

Top-error has become a popular metric for large-scale classification benchmarks due to the
inevitable semantic ambiguity among classes. Existing literature on top-optimization …

Label Ranking (LR) is an emerging non-standard supervised classification problem with
practical applications in different research fields. The Label Ranking task aims at building …

The partial label ranking problem is a particular preference learning scenario that focuses
on learning preference models from data, such that they predict a complete ranking with ties …

The problem of learning label rankings is receiving increasing attention from machine
learning and data mining community. Its goal is to learn a mapping from instances to …

The emerging of ubiquitous computing technologies in recent years has given rise to a new
field of research consisting in incorporating context-aware preference querying facilities in …

Abstract Exceptional Preferences Mining (EPM) is a crossover between two subfields of
datamining: local pattern mining and preference learning. EPM can be seen as a local …

The goal of personalization is to deliver information that is relevant to an individual or a
group of individuals in the most appropriate format and layout. In the OLAP context …