<ul><li>A new approach called the Multiplex Classification Framework has been introduced to address the complexities of classification problems through problem transformation, ontology engineering, and model ensembling.</li><li>The framework offers adaptability to any number of classes and logical constraints, a method for managing class imbalance, elimination of confidence threshold selection, and a modular structure.</li><li>Experiments comparing the Multiplex approach with conventional classification models showed significant improvement in classification performance, especially in problems with a large number of classes and class imbalances.</li><li>However, the Multiplex approach requires understanding of the problem domain and experience with ontology engineering, and involves training multiple models.</li></ul>

The Multiplex Classification Framework: optimizing multi-label classifiers through problem transformation, ontology engineering, and model ensembling

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