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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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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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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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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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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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