Meta-learning recommends the best multi-target regression method using 58 meta-features from 648 synthetic datasets, with Random Forest achieving over 70% balanced accuracy.
Clustering algorithm selection by meta-learning systems: A new distance-based problem characterization and ranking combination methods
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Towards meta-learning for multi-target regression problems
Meta-learning recommends the best multi-target regression method using 58 meta-features from 648 synthetic datasets, with Random Forest achieving over 70% balanced accuracy.