Eneldo Loza Mencía

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Technische Universität Darmstadt

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. ∙ 4 ∙ share

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Analysis and Optimization of Deep CounterfactualValue Networks

Recently a strong poker-playing algorithm called DeepStack was published...
07/02/2018 ∙ by Eneldo Loza Mencía, et al. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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Improving Outbreak Detection with Stacking of Statistical Surveillance Methods

Epidemiologists use a variety of statistical algorithms for the early de...
07/17/2019 ∙ by Moritz Kulessa, et al. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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Eneldo Loza Mencía

Featured Co-authors

Eyke Hüllermeier

Johannes Fürnkranz

Michael Rapp

Vu-Linh Nguyen

Moritz Kulessa

Yannik Klein

Patryk Hopner

On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics

Analysis and Optimization of Deep CounterfactualValue Networks

Learning Interpretable Rules for Multi-label Classification

Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules

Improving Outbreak Detection with Stacking of Statistical Surveillance Methods

Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning

Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy

On Aggregation in Ensembles of Multilabel Classifiers

Learning Gradient Boosted Multi-label Classification Rules