Abstract Decision Trees (DTs) are predictive models in supervised learning, known not only
for their unquestionable utility in a wide range of applications but also for their interpretability …

Artificial Intelligence (AI)‐based systems are widely employed nowadays to make decisions
that have far‐reaching impact on individuals and society. Their decisions might affect …

Heterogeneous tabular data are the most commonly used form of data and are essential for
numerous critical and computationally demanding applications. On homogeneous datasets …

Abstract Machine learning (ML) techniques are often employed for the accurate prediction of
the compressive strength of concrete. Despite higher accuracy, previous ML models failed to …

Counterfactual explanations provide means for prescriptive model explanations by
suggesting actionable feature changes (eg, increase income) that allow individuals to …

Imbalanced classification on bankruptcy prediction is considered as one of the most
important topics in financial institutions. In this context, various statistical and artificial …

In recent years, game-theoretic Shapley values have gained increasing attention with
respect to local model explanation by feature attributions. While the approach using Shapley …

Reward sparsity, difficult credit assignment, and misalignment are only a few of the many
issues that make it difficult, if not impossible, for deep reinforcement learning (RL) agents to …

As an effective extension of rough set theory, the variable precision neighborhood rough set
model has been applied to the attribute dependency-based improvement of decision tree …

Business owners, investors, governments, banks, securities, and other financial participants
have increasingly relied on bankruptcy prediction as a way to protect their assets. Managers …