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Found 5 papers, 1 papers with code
Multi-label Stream Classification with Self-Organizing Maps
With the lack of stationarity in the distribution of data streams, new algorithms are needed to online adapt to such changes (concept drift).
Generation of Consistent Sets of Multi-Label Classification Rules with a Multi-Objective Evolutionary Algorithm
Recently, the interest in interpretable classification models has grown, partially as a consequence of regulations such as the General Data Protection Regulation.
Preventing the Generation of Inconsistent Sets of Classification Rules
One of the problems associated with sets is that multiple rules may cover a single instance, but predict different classes for it, thus requiring a conflict resolution strategy.
Better Trees: An empirical study on hyperparameter tuning of classification decision tree induction algorithms
DT induction algorithms present high predictive performance and interpretable classification models, though many HPs need to be adjusted.
Hierarchical Multi-Label Classification Networks
One of the most challenging machine learning problems is a particular case of data classification in which classes are hierarchically structured and objects can be assigned to multiple paths of the class hierarchy at the same time.
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Hierarchical Multi-label Classification
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General Classification
Hierarchical Multi-label Classification
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