This work automates decision tree construction from neural networks.
problem Creating optimal decision tree architectures from neural networks.
method Automatic induction of non-greedy decision trees using weights transfer from neural networks.
result Improved model performance over fixed hyperparameters.
AGBoost uses attention weights to improve GBM for regression problems.
problem Improving gradient boosting machine for regression tasks.
method Attention-based modification of GBM with trainable attention weights.
result AGBoost achieves better performance on regression datasets.
The paper proposes a method to improve random forest classification accuracy by weighting trees based on their decision path reliability.
problem Random forests' uniform voting fails to correct errors in regions where incorrect tree representations outnumber correct ones.
method The paper introduces using the structural pattern of each tree's decision path as an instance-adaptive reliability signal to identify and weight more reliable trees.
result Using the proposed method yields a statistically significant accuracy improvement over RF on 36 binary classification benchmarks.
The study analyzes when Bayesian averaging over decision trees is reliable.
problem When do Bayesian model averaging weights over decision trees provide reliable information?
method Closed-form solution for Bayesian decision trees with Catalan-exponential priors.
result Established a complete non-asymptotic theory of rational commitment thresholds.
A new Bayesian Decision Tree algorithm for regression and classification.
problem Costly construction of Bayesian Decision Trees.
method General Bayesian Decision Tree algorithm without Markov Chain Monte Carlo or pruning.
result Greedy-modal tree explains most of the information in numerical examples.
Alternative to convolutions using decision trees for neural networks.
problem Replacing complex convolutions with simpler decision-based layers.
method Binary decisions as indices to conditional distributions, trained using backpropagation.
result Performance similar to conventional neural networks, with runtime improvements.
Sparse oblique decision tree improves security rules for renewable power systems.
problem Identifying secure operating conditions in power systems with high renewable energy.
method Sparse weighted oblique decision tree to learn and embed linear security rules.
result The method significantly increases secure states and reduces solution time.
Convex polytope trees expand decision trees with interpretable boundaries.
problem High accuracy often requires many nodes in decision trees, reducing interpretability.
method CPT uses logical disjunction of weighted linear decision-makers, geometrically a convex polytope.
result CPT achieves high accuracy with fewer nodes compared to existing methods.
Improved random forest models enhance machine learning predictions.
problem Equal weights for random forest base decision trees are not optimal.
method Proposes algorithms to modify weighting strategy of regular random forest.
result Numerical results show significant improvements over regular random forest.
Proposes neural decision trees combining neural networks and decision trees.
problem Combining neural networks and decision trees for improved modeling power.
method Integrates multilayer perceptrons into decision tree structure with weight sharing.
result Demonstrates improved modeling power and flexibility over standard decision trees and MLPs.
The study examines how altering impurity functions influences optimal splits in binary classification trees.
problem Understanding how altering impurity functions affects optimal splits in binary classification trees.
method Investigates how skewing impurity functions biases optimal splits towards isolating points of a particular class.
result A necessary and sufficient condition for skewing an impurity function to bias optimal splits towards isolating points of a particular class is provided.
This paper describes experiments, on two domains, to investigate the effect of averaging over predictions of multiple decision trees, instead of using a single tree. Other authors have pointed out theoretical and commonsense reasons for preferring the multiple tree approach. Ideally, we would like to consider predictio…
A new method of event reweighting using boosted decision trees is presented.
problem Quality control of reweighting step in machine learning models.
method Novel event reweighting method based on boosted decision trees.
result Improved quality control of reweighting step in machine learning models.
We discuss an autoencoder model in which the encoding and decoding functions are implemented by decision trees. We use the soft decision tree where internal nodes realize soft multivariate splits given by a gating function and the overall output is the average of all leaves weighted by the gating values on their path. …
G-FIGS uses instance weights to create interpretable models from diverse data.
problem Generalizing to diverse data distributions while maintaining interpretability.
method Estimates group membership probabilities, uses as instance weights in FIGS to grow decision trees.
result Achieves state-of-the-art prediction performance and maintains interpretability.
DTE uses tree leaf means to embed data, balancing accuracy and speed.
problem High variance in decision tree splits and computational inefficiency of ensembles.
method DTE constructs an interpretable feature representation using leaf means of a trained tree.
result DTE strikes a balance between accuracy and computational efficiency, outperforming ensembles.
Two algorithms for interpreting and boosting tree-based models using rule covering.
problem Interpreting and boosting tree-based ensemble methods.
method Mathematical programming models constructed from decision tree rules.
result Selects a few rules that closely match the accuracy of the model.
WOTBoost improves minority class accuracy in imbalanced datasets.
problem Imbalanced datasets lead to low accuracy in minority class classification.
method Combines weighted oversampling and boosting techniques.
result WOTBoost achieves best G mean and highest AUC score on multiple datasets.
A new metric learning algorithm using Deep Forest for better class separability.
problem Improving distance metrics between similar and different classes.
method Discriminative Deep Forest (DisDF) algorithm with weighted decision trees.
result The algorithm reduces distances within the same class and increases between different classes.
New weight function improves subtree kernel performance.
problem Improving performance of subtree kernel for tree data.
method Investigated the influence of weight function, defined a new learned weight function.
result Performance of subtree kernel improved when leaf weights vanish.
Decision Machines embeds decision trees into vector spaces for improved optimization.
problem Overfitting and difficulty in finding optimal decision tree structure.
method Embedding Boolean tests into a binary vector space and representing tree structure as matrices.
result Optimized decision trees with enhanced predictive power.
Infinite BART model selects number of trees and allows different functions for clusters.
problem Regression and classification analysis with automatic tree selection and cluster-specific functions.
method Incorporates an Indian Buffet process prior to select a subset of decision trees for each observation.
result Infinite BART model outperforms classic BART on simulated and real datasets.
REALITrees uses a Rashomon ensemble approach for active learning in sparse decision trees.
problem Active learning reduces labeling costs by selecting informative samples, but current methods often sacrifice model diversity and direct characterization of the hypothesis space.
method REALITrees constructs a committee of all near-optimal sparse decision tree models using a Rashomon Set and a Gibbs posterior to weight them by empirical risk.
result REALITrees outperforms randomized ensembles, especially in noisy environments, by leveraging expanded model multiplicity.
Boosting meta-trees improve decision tree performance.
problem Overfitting in decision trees.
method Boosting approach to construct multiple meta-trees.
result Ensembles of meta-trees prevent overfitting.
dtControl uses decision trees to represent controllers efficiently and explainably.
problem Representing controllers concisely and explainably.
method dtControl uses decision tree learning algorithms to represent controllers. Novel techniques for determinizing controllers are introduced.
result Novel techniques for determinizing controllers during decision tree construction are extremely efficient, yielding small decision trees.
Extremely Fast Decision Tree improves accuracy on large datasets.
problem Improving accuracy on large classification datasets.
method Hoeffding Anytime Tree, a modified version of Hoeffding Tree.
result Extremely Fast Decision Tree achieves superior prequential accuracy on most UCI datasets.
The paper develops a decision support system for hierarchical text classification of conference proceedings.
problem Classifying documents with a fixed hierarchical structure of topics.
method Developed a weighted hierarchical similarity function to calculate topic relevance, using entropy of words to estimate weights.
result The weighted hierarchical similarity function improves ranking accuracy compared to other methods.
A new decision tree method avoids overfitting without hyperparameters.
problem Overfitting in decision trees.
method Hyperparameter-free approach that constructs smaller, shallower trees.
result Avoids overfitting without compromising accuracy or training time.
Causal Interaction Trees identify treatment subgroup effects in observational data.
problem Identifying subgroups with enhanced treatment effects in observational studies.
method Extending Classification and Regression Trees with subgroup-specific treatment effect estimators.
result The proposed algorithms enhance treatment effect heterogeneity in subgroups.
Neural models can realize decision trees with parameter sharing and improved performance.
problem Training and optimizing oblique decision trees.
method Locally constant networks based on ReLU gradients, parameter sharing, and neural tools.
result Locally constant networks can implicitly model oblique decision trees with fewer neurons.
A new probability distribution on full rooted trees helps in model selection.
problem Model selection for full rooted trees is problematic due to their hierarchical structure.
method Assume a prior distribution on full rooted trees, using Bayes decision theory.
result The proposed distribution enables optimal model selection and prevents overfitting.
Attention augments forest for tabular data accuracy.
problem Training tabular data models with high accuracy and efficiency.
method Tree Attention Block (TAB) in differentiable forest framework.
result Attention augmented differentiable forest achieves comparable and sometimes higher accuracy than GBDT models.
DNDT combines neural networks and decision trees for tabular data.
problem Tabular data processing with interpretability and efficiency.
method Deep Neural Decision Trees (DNDT) using neural networks to model decision trees.
result DNDT achieves both interpretability and efficiency in tabular data processing.
Paper designs decision trees for minimizing misclassification in crowdsourcing.
problem Minimizing misclassification in crowdsourcing systems with unreliable workers.
method Proposes two algorithms for designing decision trees based on minimizing the probability of misclassification and entropy.
result Demonstrates improved error performance through worker assignment to different tests.
Trinary decision tree improves handling of missing data in machine learning.
problem Improving accuracy in decision tree algorithms when dealing with missing data.
method Introduces Trinary decision tree, which does not assume missing values contain information about the response.
result Trinary decision tree outperforms other algorithms in Missing Completely at Random settings, especially when data is only missing out-of-sample.
Optimized decision trees for reinforcement learning, improving sample complexity and interpretability.
problem Updating decision trees online in reinforcement learning.
method Gradient update over differentiable decision trees, including theoretical justification and empirical validation.
result Our approach outperforms neural networks in sample complexity and achieves higher rewards online.
Meta-learning interpretable decision trees with synthetic data.
problem Lack of efficient, scalable methods for generating synthetic data for decision tree meta-learning.
method Synthetic generation of near-optimal decision trees using the MetaTree transformer architecture.
result Meta-learning of decision trees achieves performance comparable to real-world data or optimal decision trees, with significant computational cost reduction.
Extracts decision trees from CNNs to explain concept importance.
problem Understanding how CNNs make decisions about human-understandable concepts.
method Inferring labeled concept data from CNN hidden layer activations and creating a shallow decision tree.
result Extracted decision trees accurately represent CNN classifications.
The paper improves decision tree stability for health care applications.
problem Stability of decision trees in health care applications.
method Introducing a new distance metric to determine tree stability and proposing a novel training methodology.
result On average, a 4.6% decrease in predictive power yields a 38% increase in model stability.
POWSS simplifies Q-value estimation in POMDPs with continuous observations.
problem Lack of theoretical justification for online sampling-based algorithms in POMDPs with continuous observation spaces.
method Developed POWSS, a simplified algorithm that estimates Q-values accurately with high probability and can approach optimality with increased computational power.
result POWSS provides formal theoretical guarantees for Q-value estimation in POMDPs with continuous observations.
Proposes a simple neural network model similar to gradient boosted decision trees.
problem Building a neural network equivalent to gradient boosted decision trees.
method Converts an ensemble of decision trees to a neural network, relaxes properties, and trains a simple neural network model.
result The proposed Hammock model achieves similar performance to gradient boosted decision trees.
New algorithm finds optimal decision trees for binary variables.
problem Greedy decision tree algorithms often produce suboptimal models.
method Analytical bounds and modern systems techniques co-designed for efficient search space reduction.
result First practical algorithm for optimal decision trees for binary variables.
FoLDTree improves oblique decision trees with ULDA, enhancing accuracy and feature selection.
problem Axis-orthogonal splits limit traditional decision trees' performance on oblique decision boundaries.
method Integrates ULDA into decision tree structure for efficient oblique splits, feature selection, and handling missing values.
result FoLDTree outperforms other methods in accuracy and feature selection, comparable to random forest.
Evolutionary algorithms improve decision tree ensembles.
problem Improving predictive performance of decision trees.
method Real-valued vector representation of decision trees, evolutionary algorithms (Differential evolution, Evolution strategies).
result Proposed methods outperform classical decision tree induction algorithms.
Paper introduces algorithms for private decision tree learning.
problem Private decision tree learning in distributed settings.
method Proposes DP-TopDown, NoisyCounts, and LocalRNM.
result First utility guarantees for private decision tree learning.
Enhanced ODT with Feature Concatenation boosts learning efficiency.
problem Insufficient learning efficiency of ODT due to linear projections not being transmitted to child nodes.
method Feature Concatenation ( exttt{FC-ODT}) to transmit linear projections along decision paths.
result Experiments show exttt{FC-ODT} outperforms state-of-the-art decision trees with a limited tree depth.
Transform ANNs into interpretable decision trees.
problem Lack of interpretability in ANNs.
method Developed two MDT algorithms: EC-DT and Extended C-Net.
result Extended C-Net generates the most compact and effective trees.
Max-Cut decision tree improves classification accuracy and reduces computation time.
problem Improving decision tree accuracy and efficiency for complex classification tasks.
method Alternative splitting metric (max cut) and PCA-based feature selection at each node.
result 49% improvement in accuracy with 94% reduction in CPU time on CIFAR-100 data.