Relaxed pruning biases multi-label rule induction toward larger rule heads, producing more compact models with comparable predictive performance in experiments on seven datasets.
On label dependence and loss minimization in multi-label classification
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Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning
Relaxed pruning biases multi-label rule induction toward larger rule heads, producing more compact models with comparable predictive performance in experiments on seven datasets.