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Revisiting Non-Specific Syndromic Surveillance
Infectious disease surveillance is of great importance for the preventio...
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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...
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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...
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Conformal Rule-Based Multi-label Classification
We advocate the use of conformal prediction (CP) to enhance rule-based m...
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Learning Gradient Boosted Multi-label Classification Rules
In multi-label classification, where the evaluation of predictions is le...
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On Aggregation in Ensembles of Multilabel Classifiers
While a variety of ensemble methods for multilabel classification have b...
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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...
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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...
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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...
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Improving Outbreak Detection with Stacking of Statistical Surveillance Methods
Epidemiologists use a variety of statistical algorithms for the early de...
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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...
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Learning Interpretable Rules for Multi-label Classification
Multi-label classification (MLC) is a supervised learning problem in whi...
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Analysis and Optimization of Deep CounterfactualValue Networks
Recently a strong poker-playing algorithm called DeepStack was published...
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