Multi-criterion decision making (MCDM) methods can derive alternative rankings as
solutions to decision-making problems based on survey or historical data about the …

Decision trees are popular as stand-alone classifiers or as base learners in ensemble
classifiers. Mostly, this is due to decision trees having the advantage of being easy to …

Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models.
The interpretability of decision trees motivates explainability approaches by so-called …

Heart disease seriously threatens human life due to high morbidity and mortality. Accurate
prediction and diagnosis become more critical for early prevention, detection, and treatment …

The diagnosis of Parkinson's disease (PD) is important in neurological pathology for
appropriate medical therapy. Algorithms based on decision tree induction (DTI) have been …

Explainability is essential for users to effectively understand, trust, and manage powerful
artificial intelligence solutions. Decision trees are one of the pioneer explanaible artificial …

The self-rotation of particles within cyclone separators has garnered significant attention due
to its critical role in separation processes and mass transfer enhancement. This study …

In the algorithm selection problem, where the task is to identify the most suitable solving
technique for a particular situation, most methods used as performance map** …

Supervised feature selection (FS) for classification aims at finding a more discriminative
subset from original features, to facilitate classifier training, improve classification …

When facing a classification problem, data science practitioners must search through an
armory of methods. Often, practitioners are tempted to use off-the-shelf classifiers, including …