2 citations · 3 across the 8 of their papers we have counts for
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Search Strategies for Optimal Classification and Regression Trees
Jacobus G. M. van der Linden, Mim van den Bos, Emir Demirović
Optimal decision trees (ODTs) are compact, interpretable machine learning models that globally optimize a given objective, but their scalability remains challenging. While recent w…
SORTeD Rashomon Sets of Sparse Decision Trees: Anytime Enumeration
Elif Arslan, Jacobus G. M. van der Linden, Serge Hoogendoorn +2
Sparse decision tree learning provides accurate and interpretable predictive models that are ideal for high-stakes applications by finding the single most accurate tree within a (s…
Optimal Classification Trees for Continuous Feature Data Using Dynamic Programming with Branch-and-Bound
Catalin E. Brita, Jacobus G. M. van der Linden, Emir Demirović
Computing an optimal classification tree that provably maximizes training performance within a given size limit, is NP-hard, and in practice, most state-of-the-art methods do not s…
Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance
Jacobus G. M. van der Linden, Daniël Vos, Mathijs M. de Weerdt +2
Recently there has been a surge of interest in optimal decision tree (ODT) methods that globally optimize accuracy directly, in contrast to traditional approaches that locally opti…
Optimal Survival Trees: A Dynamic Programming Approach
Tim Huisman, Jacobus G. M. van der Linden, Emir Demirović
Survival analysis studies and predicts the time of death, or other singular unrepeated events, based on historical data, while the true time of death for some instances is unknown.…
Optimal Decision Trees for Nonlinear Metrics
Emir Demirović, Peter J. Stuckey
Nonlinear metrics, such as the F1-score, Matthews correlation coefficient, and Fowlkes-Mallows index, are often used to evaluate the performance of machine learning models, in part…