A common approach to aggregate classification estimates in an ensemble o...
Classifier chains are an effective technique for modeling label dependen...
Early outbreak detection is a key aspect in the containment of infectiou...
In multi-label classification, where a single example may be associated ...
Infectious disease surveillance is of great importance for the preventio...
Arguably the key reason for the success of deep neural networks is their...
Multi-label classification is the task of assigning a subset of labels t...
We advocate the use of conformal prediction (CP) to enhance rule-based
m...
In multi-label classification, where the evaluation of predictions is le...
While a variety of ensemble methods for multilabel classification have b...
We analyze the trade-off between model complexity and accuracy for rando...
Being able to model correlations between labels is considered crucial in...
Recently, several authors have advocated the use of rule learning algori...
Epidemiologists use a variety of statistical algorithms for the early
de...
Exploiting dependencies between labels is considered to be crucial for
m...
Multi-label classification (MLC) is a supervised learning problem in whi...
Recently a strong poker-playing algorithm called DeepStack was published...