Counterexamples show HSIC feature selection misses critical features.
problem Feature selection using HSIC misses important features.
method Feature selection via HSIC maximization.
result HSIC feature selection can miss critical features.
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arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,051 papers · 148 categories
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48 results for “Feature dependence”
Proposes a new method for interpreting feature importance and effects in dependent feature models.
problem Challenges in interpreting feature importance when features are dependent and interactions are present.
method Conditional Subgroup Approach
result Conditional PFI and PDP estimates based on this approach often outperform existing methods.
GADGET framework decomposes global feature effects using recursive partitioning.
problem Misleading global feature effects when feature interactions are present.
method Generalized additive decomposition of global effects (GADGET) based on recursive partitioning.
result Minimizes interaction-related heterogeneity of local feature effects.
dGAP learns feature dependencies and predicts targets simultaneously.
problem Learning task-agnostic statistical dependencies and missing explicit feature dependencies.
method Jointly optimizes a neural dependency graph and target prediction loss.
result dGAP can recover correct feature dependencies and improve prediction accuracy.
New method disentangles feature importance scores in machine learning.
problem Misinterpretation of feature importance scores due to interactions and dependencies.
method Derive DIP (Disentangled Importance) decomposition of feature importance scores.
result DIP decomposition uniquely separates standalone contributions from interactions and dependencies.
A new framework learns system design using neural features in function space.
problem Learning system design with neural feature extractors.
method Introduces feature geometry in function space, nesting technique for optimal feature approximation.
result Optimal features found from data samples using off-the-shelf architectures and optimizers.
New methods needed for accurate feature importance due to feature dependencies.
problem Misleading variable importance measures from PaP methods due to feature dependencies.
method Alternative approaches involving additional modeling to avoid extrapolation.
result PaP metrics can over-emphasize correlated features, requiring more direct methods.
New methods using vine copulas improve accuracy of feature dependence in predictive models.
problem Inaccurate feature dependence assumptions in Shapley values lead to incorrect explanations.
method Proposed two new approaches based on vine copulas to model feature dependence.
result Vine copula approaches give more accurate approximations to true Shapley values.
Study feature representations induced by dependence between variables.
problem Learning feature representations from dependent random variables.
method Characterized sufficient and necessary conditions for dependence-induced representations, and provided a family of loss functions.
result Features learned from the family of loss functions can be expressed as the composition of a loss-dependent function and the maximal correlation function.
The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on linear dependency between input features and output values. In this pa…
Paper uses VAEAC to estimate Shapley values for complex models with mixed features.
problem Estimating Shapley values for models with dependent mixed features.
method Uses variational autoencoder with arbitrary conditioning (VAEAC) to model feature dependencies.
result VAEAC approach outperforms state-of-the-art methods for various settings.
SHAP explains boosted trees with additively modeled features.
problem Explaining predictions of boosted trees models with additively modeled features.
method SHAP values for additively modeled features in boosted trees models.
result SHAP dependence plot matches partial dependence plot for additively modeled features.
Introduces greedy feature selection for classifier-dependent feature ranking.
problem Feature selection for classification tasks.
method Greedy feature selection, identifying the most important feature at each step based on the selected classifier.
result Theoretical and numerical benefits of greedy feature selection.
Two new Hie-TAN and Hie-TAN-Lite algorithms improve TAN for hierarchical feature spaces.
problem Learning dependencies in hierarchical feature spaces.
method Exploits hierarchical parent-child relationships as constraints to learn a dependency tree.
result Hie-TAN-Lite outperforms Hie-TAN and other methods in predictive accuracy.
Improved explanation of complex models with dependent features using Kernel SHAP.
problem Accurate explanation of machine learning predictions when features are dependent.
method Extended Kernel SHAP method to handle dependent features, providing more accurate approximations to Shapley values.
result Our method gives more accurate approximations to true Shapley values in models with dependent features.
Proposes a progressive label correction method for feature-dependent label noise.
problem Real-world large-scale datasets often suffer from heterogeneous, feature-dependent label noise.
method A progressive label correction algorithm that iteratively refines the model.
result A classifier trained with this strategy converges to be consistent with the Bayes classifier for various noise patterns.
New feature selection method for multi-labeled data.
problem Feature redundancy in multi-labeled data.
method Conditional information measure based on dependency maximization.
result Efficient and fast non-parametric feature selection.
Paper proposes Gini distance statistics for estimating feature-label dependence.
problem Identifying statistical dependence between features and categorical labels.
method Generalized Gini distance in RKHS for feature-label dependence estimation.
result Gini distance statistics converge faster and have tighter error bounds than distance covariance.
Robust feature-weighted jump models for time-dependent clustering
problem Temporal clustering
method Robust feature-weighted jump model
result Accurate recovery of true cluster sequence and feature identification
The paper formalizes feature attribution to address inconsistent definitions and evaluate methods.
problem Inconsistent definitions of feature relevance in feature attribution.
method Formalization based on relaxed functional dependence, extended to instance-wise setting.
result State-of-the-art methods often fail to verify necessary properties for candidate selection.
Paper defines feature impact and importance from data, not models.
problem Misinterpretation of feature importance as impact leads to flawed insights.
method Mathematical definitions of feature impact and importance derived from partial dependence curves.
result Feature rankings by these definitions are competitive with existing techniques.
A new method reduces the number of features needed for kernel approximation from cubic to logarithmic.
problem Large datasets make kernel methods computationally expensive and impractical.
method Combines random feature maps with data-dependent feature selection to achieve Nystrom-like performance with fewer features.
result Achieves small kernel matrix approximation error and better test set accuracy with fewer features than state-of-the-art methods.
This paper improves random feature sampling using empirical leverage scores.
problem Optimizing the number of features for kernel approximation and supervised learning.
method Uses empirical leverage scores to optimize feature sampling.
result Empirical sampling of random features using leverage scores outperforms vanilla Monte Carlo sampling.
We present the Wright-Fisher Indian buffet process (WF-IBP), a probabilistic model for time-dependent data assumed to have been generated by an unknown number of latent features. This model is suitable as a prior in Bayesian nonparametric feature allocation models in which the features underlying the observed data exhi…
GRANITE: A Generalized Regional Framework for Identifying Agreement in Feature-Based Explanationsstat.ML
GRANITE unifies feature-based explanation methods to reduce disagreement.
problem Disagreement among feature-based explanation methods.
method GRANITE partitions feature space into regions minimizing interaction and distribution influences.
result Unified and consistent feature explanations.
The randomized-feature approach has been successfully employed in large-scale kernel approximation and supervised learning. The distribution from which the random features are drawn impacts the number of features required to efficiently perform a learning task. Recently, it has been shown that employing data-dependent …
Latent feature models are widely used to decompose data into a small number of components. Bayesian nonparametric variants of these models, which use the Indian buffet process (IBP) as a prior over latent features, allow the number of features to be determined from the data. We present a generalization of the IBP, the …
Identifies feature relevance bounds for ordinal regression models.
problem Interpreting ordinal regression models is challenging due to variable dependencies.
method Identifies feature relevance bounds explicitly differentiating between strongly and weakly relevant features.
result Identification of feature relevance bounds for ordinal regression models.
Study reveals LLM personas have two distinct components: frame-robust aggregated traits and frame-dependent geometric features.
problem Evaluation of LLM personas via psychometric questionnaires discards within-instance correlation structure.
method Constructed within-instance correlation matrices from IPIP-50 responses and analyzed geometry on SPD manifolds under manipulated question orderings.
result Persona expression comprises two dissociable components: aggregated features (Big Five scores) and geometric features (SPD manifold).
On the influence of dependent features in classification problems: a game-theoretic perspectivemath.OC
New measure of feature influence in classification problems considering feature dependencies.
problem Measuring the influence of features in classification problems with dependencies.
method Developed a new measure based on cooperative game theory, providing axiomatic characterization and demonstrating its equivalence to the Banzhaf-Owen value.
result The proposed influence measure effectively characterizes feature importance in classification problems with feature dependencies.
TCMI assesses mutual dependence of continuous variables without parametric assumptions.
problem Estimating mutual information from continuous distributions.
method TCMI extends mutual information to continuous variables using cumulative distributions.
result TCMI facilitates feature selection and ranking of variable sets.
NGMs create mirrored features to assess neural network feature importance.
problem Lack of feature relevance information in DNNs limits their applicability.
method Structured perturbation and kernel-based conditional dependence measure for feature importance evaluation.
result Controls feature selection error rate and maintains high selection power with correlated features.
Neural networks learn task-specific features, influenced by nonlinearity.
problem Understanding the nature of task-dependent feature learning in neural networks.
method Investigation of fully-connected, wide neural networks using Bayesian framework.
result The nature of internal representations depends on neuronal nonlinearity, leading to analog, redundant, or sparse coding schemes.
New approach interprets machine learning models through feature space transformations.
problem Interpreting models with strongly dependent features in high-dimensional spaces.
method Feature space transformations, including PCA and partial orthogonalization.
result Enhances model interpretation tools for domain experts.
CoI framework models clinical feature interactions, revealing temporal dependencies and enhancing transparency.
problem Capturing latent, time-varying dependencies among clinical features in time-series data.
method Chain-of-Influence (CoI) framework constructs an explicit, time-unfolded graph of feature interactions.
result Achieves state-of-the-art predictive performance (AUROC of 0.960 on CKD progression and 0.950 on ICU mortality).
The paper proposes a method to analyze categorical feature interactions in large datasets using graph covariance and LLMs.
problem Analyzing complex datasets with numerous categorical features and timestamps.
method Binarization of categorical features using one-hot encoding, computation of graph covariance, identifying significant feature pairs, and using LLMs to generate explanations.
result The method identifies meaningful feature pairs and potential data stories underlying categorical feature interactions.
New features generated from kernel methods are minimally dependent on sensitive features.
problem Generating fair features in the presence of sensitive and non-sensitive features.
method Relaxed Maximum Mean Discrepancy criterion, Hilbert-space-valued conditional expectation, plug-in approach.
result Closed-form solution for minimizing dependencies between new and sensitive features.
A single pre-trained agent guides feature selection using knockoffs.
problem Feature selection challenges in AI-readiness of data.
method Generates knockoff features and uses reinforcement learning.
result Optimal feature subset identified with reduced dependency on target variable.
EHBOS enhances HBOS by capturing feature interactions, improving anomaly detection.
problem Limited ability of HBOS to detect anomalies in datasets with feature interactions.
method Incorporates two-dimensional histograms to capture feature pair dependencies.
result EHBOS outperforms HBOS on datasets with critical feature interactions, achieving notable improvements in ROC AUC.
A novel feature representation method for non-image based features.
problem Inability of Convolutional Neural Networks for non-image based features or features without spatial correlations.
method REFINED: Representation of Features as Images with Neighborhood Dependencies.
result Higher prediction accuracy compared to existing methodologies.
Study reveals class-dependent effects in perturbation-based feature attribution metrics for time series classification.
problem Varying effectiveness of perturbation-based metrics across different classes in time series models.
method Systematic empirical analysis across multiple datasets, model architectures, and perturbation strategies.
result Perturbation-based metrics show varying effectiveness across classes, with some metrics performing better for certain classes.
Improves signature verification accuracy using deep CNN features.
problem Handwritten signature verification accuracy.
method Deep CNN features combined with writer-independent SVM classifier.
result Proposed approach outperforms other WI-HSV methods.
A new method accounts for predictor dependencies in XAI feature rankings.
problem Assumption of predictor independence in XAI methods leads to unreliable feature rankings.
method Proposes a proxy method to modify XAI outcomes considering predictor dependencies.
result Allows more accurate feature ranking in models with correlated predictors.
Bidirectional Quaternion Long-Short Term Memory Recurrent Neural Networks for Speech Recognitioneess.AS
QLSTM improves speech recognition by considering internal quaternion dependencies.
problem Weak internal dependencies in traditional RNNs for speech recognition.
method Proposes QLSTM, a quaternion-based LSTM that considers both external and internal dependencies.
result QLSTM achieves better performance with up to 2.8 times fewer parameters.
CSD improves goodness-of-fit testing for higher-order dependence.
problem Insensitivity of standard KSDs to higher-order dependence features like tail dependence.
method Introduces Copula-Stein Discrepancy (CSD) that targets dependence geometry directly on copula density.
result CSD is sensitive to differences in tail dependence coefficients and metrizes weak convergence of copula distributions.
Analyzes error sources in global feature effect estimation methods.
problem Unexplored error sources in global feature effect estimation methods.
method Systematic, estimator-level analysis of bias and variance.
result Holdout data is theoretically cleanest, but estimation variance depends on sample size and model characteristics.
GANs generate realistic cyber-attack alerts with feature dependencies.
problem Challenges in creating realistic cyber-attack alert data.
method Used Generative Adversarial Networks (GANs) to learn complex data distributions.
result GANs successfully generate realistic alerts with feature dependencies.
A new method explains mixed features for predictive models using conditional inference trees.
problem Explaining complex machine learning models with mixed features.
method Proposes a method to explain mixed features (continuous, discrete, ordinal, categorical) using conditional inference trees.
result Our method often outperforms current industry standards in various simulation studies and real-world financial data.