In multi-label rule learning, the best consistency-coverage tradeoff of the scoring heuristic depends on the target measure, and locally optimal rules do not always yield globally optimal models.
An evolutionary multi label classification using associative rule mining for spatial preferences
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On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics
In multi-label rule learning, the best consistency-coverage tradeoff of the scoring heuristic depends on the target measure, and locally optimal rules do not always yield globally optimal models.