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 …

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 …

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

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 …

Ensemble of label ranking trees (LRTs) are currently the state‐of‐the‐art approaches to the
label ranking problem. Recently, bagging, boosting, and random forest methods have been …

We survey multi-label ranking tasks, specifically multi-label classification and label ranking
classification. We highlight the unique challenges, and re-categorize the methods, as they …

While most existing multilabel ranking methods assume the availability of a single objective
label ranking for each instance in the training set, this paper deals with a more common …

The datasets used in machine learning and statistics are huge and often imperfect, eg, they
contain corrupted data, examples with wrong labels, or hidden biases. Most existing …

In this thesis we theoretically study questions in the area of Reliable Machine Learning in
order to design algorithms that are robust to bias and noise (Robust Machine Learning) and …