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10/18/2021
Correlation-based Discovery of Disease Patterns for Syndromic Surveillance
Early outbreak detection is a key aspect in the containment of infectiou...
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06/22/2021
Gradient-based Label Binning in Multi-label Classification
In multi-label classification, where a single example may be associated ...
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12/08/2020
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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11/02/2020
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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06/23/2020
Learning Gradient Boosted Multi-label Classification Rules
In multi-label classification, where the evaluation of predictions is le...
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06/21/2020
On Aggregation in Ensembles of Multilabel Classifiers
While a variety of ensemble methods for multilabel classification have b...
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11/11/2019
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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08/19/2019
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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08/08/2019
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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12/14/2018
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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11/30/2018