Deep neural networks are powerful tools to detect hidden patterns in data and leverage
them to make predictions, but they are not designed to understand uncertainty and estimate …

Label ranking aims to learn a mapping from instances to rankings over a finite number of
predefined labels. Random forest is a powerful and one of the most successful general …

Adapting to dynamic environments is essential for artificial agents, especially those aiming
to communicate with people interactively. In this context, a social robot that adapts its …

Decision tree learning is among the most popular and most traditional families of machine
learning algorithms. While these techniques excel in being quite intuitive and interpretable …

We propose to solve a label ranking problem as a structured output regression task. In this
view, we adopt a least square surrogate loss approach that solves a supervised learning …

Label ranking is a machine learning task that deals with mapping an instance to a ranking of
labels, representing the labels' ordered relevance to the instance. Three recent studies have …

Label Ranking (LR) is the supervised task of learning a sorting function that maps feature
vectors $ x\in\mathbb {R}^ d $ to rankings $\sigma (x)\in\mathbb S_k $ over a finite set of $ k …

Label ranking tasks are concerned with the problem of ranking a finite set of labels for each
instance according to their relevance. Boosting is a well-known and reliable ensemble …

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 …