The most popular approach for analyzing survival data is the Cox regression model. The Cox model may, however, be misspecified, and its proportionality assumption may not always be fulfilled. An alternative approach for survival prediction is random forests for survival outcomes. The standard split criterion for random…
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48 results for “split selection”
FoLDTree: A ULDA-Based Decision Tree Framework for Efficient Oblique Splits and Feature Selectioncs.LG
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.
Improves decision tree performance by correcting split selection errors.
problem Invalid statistical guarantees in split selection for decision trees.
method Introduces anytime-valid inference to provide valid statistical guarantees.
result Provides anytime-valid control of false splits under arbitrary data streams.
Novel methods for splitting Gaussian mixtures improve uncertainty propagation in nonlinear systems.
problem Improving accuracy and efficiency in nonlinear uncertainty propagation.
method Preserving mean and covariance, novel heuristics for selecting splitting direction informed by initial uncertainty and nonlinear function properties.
result Improved accuracy and efficiency in uncertainty propagation compared to existing techniques.
A new method for kernel tests without data splitting increases power.
problem Lack of power in kernel-based tests due to data splitting.
method Selective inference framework to learn hyperparameters and test on full sample.
result Empirically larger test power without data splitting, regardless of split proportion.
Recursive partitioning approaches producing tree-like models are a long standing staple of predictive modeling, in the last decade mostly as ``sub-learners'' within state of the art ensemble methods like Boosting and Random Forest. However, a fundamental flaw in the partitioning (or splitting) rule of commonly used tre…
Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid p-values are not available. An exception is a recent proposal by Wasserman and Roeder (2008) which splits the data into two pa…
The study examines stock splits and their effects on companies, managers, and shareholders.
problem Misunderstandings and confounding factors around stock splits and their impacts.
method Selected database analysis of nine recent events, examining market impact, trading volume, and shareholder base.
result Stock splits enhance trading volume, increase shareholder base, and improve market liquidity.
A method to split a data point into two parts that individually cannot reconstruct the whole, but together can.
problem Splitting a single data point into two parts such that neither can reconstruct the whole but together can.
method Borrowing ideas from Bayesian inference to achieve a continuous analog of data splitting.
result A method to achieve data fission, enabling post-selection inference in finite samples.
We propose a tree regularization framework, which enables many tree models to perform feature selection efficiently. The key idea of the regularization framework is to penalize selecting a new feature for splitting when its gain (e.g. information gain) is similar to the features used in previous splits. The regularizat…
We propose a new algorithm called PLUTO for building logistic regression trees to binary response data. PLUTO can capture the nonlinear and interaction patterns in messy data by recursively partitioning the sample space. It fits a simple or a multiple linear logistic regression model in each partition. PLUTO employs th…
MaxRR efficiently unlearns models by splitting and selecting core samples.
problem Efficiently unlearning models while verifying unlearning guarantees.
method Model splitting and core sample selection with a generalized unlearning metric.
result MaxRR achieves efficient unlearning with properties matching full retraining.
Minimal Morse functions on Poincaré dodecahedral space are selected via spectral properties.
problem Identifying minimal Morse functions on the Poincaré dodecahedral space.
method Spectral selection property P, obstruction principle, conformal variations, finite dimensional reduction.
result Restoration of minimal Morse selection on the Poincaré dodecahedral space via spectral mechanisms.
Model Class Selectionstat.ME
Introduces model class selection to find sets of near-optimal models.
problem Finding sets of near-optimal models within multiple model collections.
method Generalizes model set selection framework to model class selection, using data splitting approaches.
result Shows that simpler, interpretable models can perform similarly to complex machine learning models.
While many statistical models and methods are now available for network analysis, resampling network data remains a challenging problem. Cross-validation is a useful general tool for model selection and parameter tuning, but is not directly applicable to networks since splitting network nodes into groups requires delet…
Information gain ratio correction: Improving prediction with more balanced decision tree splitsstat.ML
Decision trees algorithms use a gain function to select the best split during the tree's induction. This function is crucial to obtain trees with high predictive accuracy. Some gain functions can suffer from a bias when it compares splits of different arities. Quinlan proposed a gain ratio in C4.5's information gain fu…
Centroid Decision Foreststat.ML
CDF uses centroids to split features for high-dimensional classification.
problem High-dimensional classification problems with complex class structures.
method CDF introduces a centroid-driven splitting strategy in decision trees.
result CDF outperforms conventional methods in high-dimensional classification.
New method selects best HTE estimator without ground-truth treatment effects.
problem Selecting best HTE estimator from multiple candidates.
method Cross-fitted, exponentially weighted test statistic with two-way sample splitting.
result Empirically, reliable error control and reduced false selections.
We present FIESTA, a model selection approach that significantly reduces the computational resources required to reliably identify state-of-the-art performance from large collections of candidate models. Despite being known to produce unreliable comparisons, it is still common practice to compare model evaluations base…
In this work, we propose a novel node splitting method for regression trees and incorporate it into the regression forest framework. Unlike traditional binary splitting, where the splitting rule is selected from a predefined set of binary splitting rules via trial-and-error, the proposed node splitting method first fin…
Novel Bayesian model improves EEG-based BCI character selection.
problem Accurately identifying target-related responses in EEG-based BCIs.
method Probit-link Split-and-merge Gaussian Process (P-SMGP) prior for feature selection.
result Reduces computational complexity and provides interpretable statistical interpretations.
CIT and CIF improve feature selection for downstream prediction.
problem Feature selection bias in machine learning models.
method Conditional inference trees and forests with Bonferroni correction.
result CIF ranks top 3 among 18 regression methods and top 4 among 17 classification methods.
The paper explores how splitting data samples influences optimal neural network hyperparameters.
problem Understanding the effectiveness of neural networks and their hyperparameters.
method Investigates the role of sample splitting in neural network hyperparameter selection.
result Optimal hyperparameters derived from sample splitting lead to a neural network model that minimizes prediction risk asymptotically.
Optimized Conformal Selection: Powerful Selective Inference After Conformity Score Optimizationstat.ME
OptCS optimizes model selection after conformal inference, controlling FDR and power loss.
problem Challenges in model selection for conformal inference, especially when limited labeled data and many model choices are available.
method OptCS framework that allows valid statistical testing after flexible data-driven model optimization, using novel multiple testing procedures.
result Valid conformal p-values constructed despite substantial data reuse, maintaining FDR control.
Optimizes data splitting for shorter conformal prediction intervals.
problem Minimizing prediction interval length while maintaining coverage.
method Theoretical framework for optimal data splitting in split conformal prediction.
result Analytical characterizations of length-optimal split ratios in various settings.
FREEtree: A Tree-based Approach for High Dimensional Longitudinal Data With Correlated Featuresstat.ML
FREEtree improves tree-based methods for correlated longitudinal data.
problem Poor performance of Random Forests in high dimensional longitudinal data with correlated features.
method FREEtree uses a piecewise random effects model and clustering with WGCNA to select features and maintain interpretability.
result FREEtree outperforms other tree-based methods in prediction and feature selection accuracy.
In the regression setting, given a set of hyper-parameters, a model-estimation procedure constructs a model from training data. The optimal hyper-parameters that minimize generalization error of the model are usually unknown. In practice they are often estimated using split-sample validation. Up to now, there is an ope…
Random forest hyperparameters affect variable selection in omics studies.
problem Impact of hyperparameters on variable selection in random forests.
method Two simulation studies using theoretical and empirical data.
result Hyperparameters influence variable selection more than the splitting strategy and sample fraction.
Pipeline selects best RL policy from limited data.
problem Selecting best RL policy from small datasets.
method Task- and method-agnostic pipeline using multiple data splits.
result Pipeline produces higher-performing policies.
Exact selective inference with randomization for Gaussian regression models.
problem Exact selective inference in Gaussian regression models.
method Introduces a pivot for exact selective inference with randomization, reducing the problem to a bivariate truncated Gaussian distribution.
result Our pivot leads to exact inference and produces narrower confidence intervals than related methods.
New method splits unknown covariance Gaussians into independent parts.
problem Splitting multivariate Gaussian data with unknown covariance.
method Developed a general algorithm for decomposing unknown covariance Gaussians.
result Demonstrated decomposition for single multivariate Gaussian with unknown covariance.
Survival trees exhibit end-cut preference, leading to biased splits.
problem End-cut preference in survival trees causes biased splits and poor interpretability.
method Proposed a smooth sigmoid surrogate (SSS) approach to replace hard-threshold indicator function.
result Smooth sigmoid surrogate (SSS) effectively mitigates end-cut preference in survival trees.
We study the convergence of the predictive surface of regression trees and forests. To support our analysis we introduce a notion of adaptive concentration for regression trees. This approach breaks tree training into a model selection phase in which we pick the tree splits, followed by a model fitting phase where we f…
Despite the impressive performance of random forests (RF), its theoretical properties have not been thoroughly understood. In this paper, we propose a novel RF framework, dubbed multinomial random forest (MRF), to analyze the \emph{consistency} and \emph{privacy-preservation}. Instead of deterministic greedy split rule…
Paper proposes adaptive parameter selection for KGD algorithms.
problem Improving parameter selection for kernel-based gradient descent.
method Integrates bias-variance analysis with splitting method, introduces empirical effective dimension.
result Adaptive parameter selection strategy achieves optimal generalization error bound.
Reshuffling splits improves hyperparameter optimization's generalization performance.
problem Improving peak performance of machine learning models through better hyperparameter optimization.
method Reshuffling splits for every hyperparameter configuration improves generalization performance.
result Reshuffling leads to better generalization performance compared to fixed splits.
A system of nested dichotomies is a method of decomposing a multi-class problem into a collection of binary problems. Such a system recursively applies binary splits to divide the set of classes into two subsets, and trains a binary classifier for each split. Many methods have been proposed to perform this split, each …
Causal trees struggle with accuracy in estimating treatment effects.
problem Estimating heterogeneous causal treatment effects using recursive decision trees.
method Adaptive recursive partitioning with and without sample splitting.
result Causal tree estimators can have uniform-norm errors decreasing more slowly than any power of the sample size.
HATT improves online decision tree ensembles by using a more eager splitting strategy.
problem Improving the efficiency of online decision tree ensembles.
method Replacing Hoeffding Tree's split strategy with HATT, which uses the Hoeffding Test for candidate splits.
result HATT outperforms Hoeffding Tree in online bagging and boosting ensembles, as shown by significant performance improvements in various testbenches.
Bayesian approach controls FDR in high-dimensional models.
problem High-dimensional variable selection and inference.
method Adapted Mirror Statistic to Bayesian framework for FDR control.
result Effective FDR control without data splitting.
Significance-First Splitting: Aligning Treatment Heterogeneity Detection with Honest Estimationstat.ME
A hybrid algorithm fuses significance-based splitting with honest sample-splitting for estimating heterogeneous treatment effects.
problem Estimating heterogeneous treatment effects while maintaining valid inference.
method Significance-first splitting using a squared -statistic for treatment side interaction.
result Achieves approximately 90% CI coverage at the 90% nominal level across various synthetic designs and datasets.
Boosting as gradient descent algorithms is one popular method in machine learning. In this paper a novel Boosting-type algorithm is proposed based on restricted gradient descent with structural sparsity control whose underlying dynamics are governed by differential inclusions. In particular, we present an iterative reg…
Decision tree classifiers are a widely used tool in data stream mining. The use of confidence intervals to estimate the gain associated with each split leads to very effective methods, like the popular Hoeffding tree algorithm. From a statistical viewpoint, the analysis of decision tree classifiers in a streaming setti…
LEARN-SAM improves RL from sub-optimal demonstrations by localizing expert policies and selectively using demonstrations.
problem Improving RL from sub-optimal or sparse demonstrations.
method Local Ensemble and Reparameterization with Split and Merge of expert policies (LEARN-SAM).
result LEARN-SAM boosts learning speed and accuracy by selectively using demonstrations.
Paper improves anomaly detection by using non-uniform random choices in isolation forests.
problem Detecting clustered diverse outliers more effectively.
method Comparing different split guiding criteria in isolation forests.
result Non-uniform random choices improve outlier discrimination for certain outlier classes.
The paper introduces a new model to correct bias in treatment effect estimates due to sample selection.
problem Bias in treatment effect estimates due to sample selection.
method Type 2 Tobit Bayesian Additive Regression Trees (TOBART-2) with Dirichlet Process Mixture distribution and soft trees.
result Corrects bias in treatment effect estimates by accounting for nonlinearities and model uncertainty.
Data thinning splits observations into independent parts for convolution-closed distributions.
problem Validation of unsupervised learning results in settings with limited data.
method Data thinning, splitting observations into independent parts following the same distribution.
result Data thinning provides an attractive alternative to cross-validation in settings with limited sample splitting.
A system of nested dichotomies is a method of decomposing a multi-class problem into a collection of binary problems. Such a system recursively splits the set of classes into two subsets, and trains a binary classifier to distinguish between each subset. Even though ensembles of nested dichotomies with random structure…