
-
Learning Structured Declarative Rule Sets – A Challenge for Deep Discrete Learning
Arguably the key reason for the success of deep neural networks is their...
read it
-
A Flexible Class of Dependence-aware Multi-Label Loss Functions
Multi-label classification is the task of assigning a subset of labels t...
read it
-
Learning Gradient Boosted Multi-label Classification Rules
In multi-label classification, where the evaluation of predictions is le...
read it
-
On Aggregation in Ensembles of Multilabel Classifiers
While a variety of ensemble methods for multilabel classification have b...
read it
-
Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy
We analyze the trade-off between model complexity and accuracy for rando...
read it
-
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning
Being able to model correlations between labels is considered crucial in...
read it
-
On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics
Recently, several authors have advocated the use of rule learning algori...
read it
-
Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules
Exploiting dependencies between labels is considered to be crucial for m...
read it
-
Learning Interpretable Rules for Multi-label Classification
Multi-label classification (MLC) is a supervised learning problem in whi...
read it