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Learning Structured Declarative Rule Sets – A Challenge for Deep Discrete Learning
Arguably the key reason for the success of deep neural networks is their...
12/08/2020 ∙ by Johannes Fürnkranz, et al. ∙
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A Flexible Class of Dependence-aware Multi-Label Loss Functions
Multi-label classification is the task of assigning a subset of labels t...
11/02/2020 ∙ by Eyke Hüllermeier, et al. ∙
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Learning Gradient Boosted Multi-label Classification Rules
In multi-label classification, where the evaluation of predictions is le...
06/23/2020 ∙ by Michael Rapp, et al. ∙
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On Aggregation in Ensembles of Multilabel Classifiers
While a variety of ensemble methods for multilabel classification have b...
06/21/2020 ∙ by Vu-Linh Nguyen, et al. ∙
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Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy
We analyze the trade-off between model complexity and accuracy for rando...
11/11/2019 ∙ by Michael Rapp, et al. ∙
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Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning
Being able to model correlations between labels is considered crucial in...
08/19/2019 ∙ by Yannik Klein, et al. ∙
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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...
08/08/2019 ∙ by Michael Rapp, et al. ∙
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Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules
Exploiting dependencies between labels is considered to be crucial for m...
12/14/2018 ∙ by Michael Rapp, et al. ∙
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Learning Interpretable Rules for Multi-label Classification
Multi-label classification (MLC) is a supervised learning problem in whi...
11/30/2018 ∙ by Eneldo Loza Mencía, et al. ∙
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