Tootfinder

Improving Decision Trees through the Lens of Parameterized Local Search
Juha Harviainen, Frank Sommer, Manuel Sorge
https://arxiv.org/abs/2510.12726 https://

Improving Decision Trees through the Lens of Parameterized Local Search
Algorithms for learning decision trees often include heuristic local-search operations such as (1) adjusting the threshold of a cut or (2) also exchanging the feature of that cut. We study minimizing the number of classification errors by performing a fixed number of a single type of these operations. Although we discover that the corresponding problems are NP-complete in general, we provide a comprehensive parameterized-complexity analysis with the aim of determining those properties of the pr…

Discovering interpretable piecewise nonlinear model predictive control laws via symbolic decision trees
Ilias Mitrai
https://arxiv.org/abs/2510.10411 https://

Discovering interpretable piecewise nonlinear model predictive control laws via symbolic decision trees
In this paper, we propose symbolic decision trees as surrogate models for approximating model predictive control laws. The proposed approach learns simultaneously the partition of the input domain (splitting logic) as well as local nonlinear expressions for predicting the control action leading to interpretable piecewise nonlinear control laws. The local nonlinear expressions are determined by the learning problem and are modeled using a set of basis functions. The learning task is posed as a m…

Handling Missing Data in Probabilistic Regression Trees: Methods and Implementation in R
Taiane Schaedler Prass, Alisson Silva Neimaier, Guilherme Pumi
https://arxiv.org/abs/2510.03634

Handling Missing Data in Probabilistic Regression Trees: Methods and Implementation in R
Probabilistic Regression Trees (PRTrees) generalize traditional decision trees by incorporating probability functions that associate each data point with different regions of the tree, providing smooth decisions and continuous responses. This paper introduces an adaptation of PRTrees capable of handling missing values in covariates through three distinct approaches: (i) a uniform probability method, (ii) a partial observation approach, and (iii) a dimension-reduced smoothing technique. The prop…

Inductive inference of gradient-boosted decision trees on graphs for insurance fraud detection
F\'elix Vandervorst, Bruno Deprez, Wouter Verbeke, Tim Verdonck
https://arxiv.org/abs/2510.05676

Inductive inference of gradient-boosted decision trees on graphs for insurance fraud detection
Graph-based methods are becoming increasingly popular in machine learning due to their ability to model complex data and relations. Insurance fraud is a prime use case, since false claims are often the result of organised criminals that stage accidents or the same persons filing erroneous claims on multiple policies. One challenge is that graph-based approaches struggle to find meaningful representations of the data because of the high class imbalance present in fraud data. Another is that insu…

Efficient & Correct Predictive Equivalence for Decision Trees
Joao Marques-Silva, Alexey Ignatiev
https://arxiv.org/abs/2509.17774 https://arxiv.org/pd…

Efficient & Correct Predictive Equivalence for Decision Trees
The Rashomon set of decision trees (DTs) finds importance uses. Recent work showed that DTs computing the same classification function, i.e. predictive equivalent DTs, can represent a significant fraction of the Rashomon set. Such redundancy is undesirable. For example, feature importance based on the Rashomon set becomes inaccurate due the existence of predictive equivalent DTs, i.e. DTs with the same prediction for every possible input. In recent work, McTavish et al. proposed solutions for s…

Leaf it to renewal: Improved predictive maintenance policies via renewal theory and decision trees
Daniel Koutas, Daniel Straub
https://arxiv.org/abs/2509.20145 https://

Leaf it to renewal: Improved predictive maintenance policies via renewal theory and decision trees
We propose a general method for deriving prognostics-based predictive maintenance policies. The method takes into account the available decision options at hand, the information on the future state of the system provided by a prognostic model, as well as the costs of the underlying renewal-reward process. It results in heuristic policies with only a few parameters, which can be determined based on theoretical considerations or by optimization from run-to-failure data. We show the potential of t…

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Energy Equity, Infrastructure and Demographic Analysis with XAI Methods
Sarahana Shrestha, Aparna S. Varde, Pankaj Lal
https://arxiv.org/abs/2509.16279 https://

Energy Equity, Infrastructure and Demographic Analysis with XAI Methods
This study deploys methods in explainable artificial intelligence (XAI), e.g. decision trees and Pearson's correlation coefficient (PCC), to investigate electricity usage in multiple locales. It addresses the vital issue of energy burden, i.e. total amount spent on energy divided by median household income. Socio-demographic data is analyzed with energy features, especially using decision trees and PCC, providing explainable predictors on factors affecting energy burden. Based on the results of…

Foundational theory for optimal decision tree problems. II. Optimal hypersurface decision tree algorithm
Xi He
https://arxiv.org/abs/2509.12057 https://arx…

Foundational theory for optimal decision tree problems. II. Optimal hypersurface decision tree algorithm
Decision trees are a ubiquitous model for classification and regression tasks due to their interpretability and efficiency. However, solving the optimal decision tree (ODT) problem remains a challenging combinatorial optimization task. Even for the simplest splitting rules--axis-parallel hyperplanes--it is NP-hard to optimize. In Part I of this series, we rigorously defined the proper decision tree model through four axioms and, based on these, introduced four formal definitions of the ODT prob…

Fr\'echet Geodesic Boosting
Yidong Zhou, Su I Iao, Hans-Georg M\"uller
https://arxiv.org/abs/2509.18013 https://arxiv.org/pdf/2509.18013

Fréchet Geodesic Boosting
Gradient boosting has become a cornerstone of machine learning, enabling base learners such as decision trees to achieve exceptional predictive performance. While existing algorithms primarily handle scalar or Euclidean outputs, increasingly prevalent complex-structured data, such as distributions, networks, and manifold-valued outputs, present challenges for traditional methods. Such non-Euclidean data lack algebraic structures such as addition, subtraction, or scalar multiplication required b…

The Honest Truth About Causal Trees: Accuracy Limits for Heterogeneous Treatment Effect Estimation
Matias D. Cattaneo, Jason M. Klusowski, Ruiqi Rae Yu
https://arxiv.org/abs/2509.11381

The Honest Truth About Causal Trees: Accuracy Limits for Heterogeneous Treatment Effect Estimation
Recursive decision trees have emerged as a leading methodology for heterogeneous causal treatment effect estimation and inference in experimental and observational settings. These procedures are fitted using the celebrated CART (Classification And Regression Tree) algorithm [Breiman et al., 1984], or custom variants thereof, and hence are believed to be "adaptive" to high-dimensional data, sparsity, or other specific features of the underlying data generating process. Athey and Imbens [2016] pr…

Fairness-Aware and Interpretable Policy Learning
Nora Bearth, Michael Lechner, Jana Mareckova, Fabian Muny
https://arxiv.org/abs/2509.12119 https://arxiv.o…

Fairness-Aware and Interpretable Policy Learning
Fairness and interpretability play an important role in the adoption of decision-making algorithms across many application domains. These requirements are intended to avoid undesirable group differences and to alleviate concerns related to transparency. This paper proposes a framework that integrates fairness and interpretability into algorithmic decision making by combining data transformation with policy trees, a class of interpretable policy functions. The approach is based on pre-processing…

ACT: Agentic Classification Tree
Vincent Grari, Tim Arni, Thibault Laugel, Sylvain Lamprier, James Zou, Marcin Detyniecki
https://arxiv.org/abs/2509.26433 https://

ACT: Agentic Classification Tree
When used in high-stakes settings, AI systems are expected to produce decisions that are transparent, interpretable, and auditable, a requirement increasingly expected by regulations. Decision trees such as CART provide clear and verifiable rules, but they are restricted to structured tabular data and cannot operate directly on unstructured inputs such as text. In practice, large language models (LLMs) are widely used for such data, yet prompting strategies such as chain-of-thought or prompt op…

Spatio-Temporal Directed Graph Learning for Account Takeover Fraud Detection
Mohsen Nayebi Kerdabadi, William Andrew Byron, Xin Sun, Amirfarrokh Iranitalab
https://arxiv.org/abs/2509.20339

Spatio-Temporal Directed Graph Learning for Account Takeover Fraud Detection
Account Takeover (ATO) fraud poses a significant challenge in consumer banking, requiring high recall under strict latency while minimizing friction for legitimate users. Production systems typically rely on tabular gradient-boosted decision trees (e.g., XGBoost) that score sessions independently, overlooking the relational and temporal structure of online activity that characterizes coordinated attacks and "fraud rings." We introduce ATLAS (Account Takeover Learning Across Spatio-Temporal Dire…