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 …

We provide a new formulation for the problem of learning the optimal classification tree of a
given depth as a binary linear program. A limitation of previously proposed Mathematical …

Business analytics use techniques from data science, data mining, artificial intelligence
(especially, machine learning), mathematics and statistics to gain insights and …

Current state-of-the-art algorithms for training robust decision trees have high runtime costs
and require hours to run. We present GROOT, an efficient algorithm for training robust …

Several recent publications report advances in training optimal decision trees (ODTs) using
mixed-integer programs (MIPs), due to algorithmic advances in integer programming and a …

Decision trees are a popular choice of explainable model, but just like neural networks, they
suffer from adversarial examples. Existing algorithms for fitting decision trees robust against …

The query tuning, which is mostly found in the syntax semantics of fuzzy logic inference
algorithms, uses the query syntax of the boolean data type. Fuzzy logic inference is …

Fuzzy logic is an extension of Boolean logic created by Lotfi Zadeh in 1965 based on his
mathematical theory of fuzzy sets, which is a generalization of classical set theory. By …

This paper explores the use of Column Generation (CG) techniques in constructing
univariate binary decision trees for classification tasks. We propose a novel Integer Linear …

The application of machine learning in daily life requires interpretability and robustness. In
this paper we try to make the process of building robust and interpretable decision trees …