Incorporating feature selection into a classification or regression method often carries a number of advantages. In this paper we formalize feature selection specifically from a discriminative perspective of improving classification/regression accuracy. The feature selection method is developed as an extension to the r…
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48 results for “feature extension”
In this paper, a genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to …
Deep learning directly extracts features for emotion recognition, outperforming traditional methods.
problem Emotion recognition using traditional feature encoding methods.
method Used deep learning networks to directly encode features for emotion recognition.
result Highest performance in emotion recognition on EmoDB dataset.
A simple model for unbalanced optimal transport captures key features.
problem Capturing the main features of unbalanced optimal transport.
method Introducing a metric on the conical extension of diffeomorphisms and studying its properties.
result Total mass evolves with constant acceleration along geodesics.
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.
Paper proposes a loss extension for neural networks to improve OSR performance.
problem Open set recognition problem, distinguishing known and unknown classes.
method Introduces a loss function extension to find more discriminative polar representations.
result Significantly improves performance on datasets from different domains.
The paper proposes a method to test features selected by SeqFS-DA with controlled FPR.
problem Ensuring reliability of feature selection after domain adaptation in high-dimensional regression.
method Proposes a novel method to test features selected by SeqFS-DA with controlled FPR.
result The proposed method controls FPR below a significance level (e.g., 0.05) and enhances statistical power.
AdaEnsemble learns adaptive feature interactions for CTR prediction.
problem Learning feature interactions for CTR prediction in recommender systems and Ads ranking.
method AdaEnsemble is a Sparsely-Gated Mixture-of-Experts (SparseMoE) architecture that dynamically selects feature interaction depth.
result AdaEnsemble achieves better prediction accuracy and inference efficiency compared to state-of-the-art models.
New algorithm adds discretized features to improve predictive models.
problem Improving predictive models using continuous features.
method D-MIAT algorithm for supervised discretization and feature extension.
result Combination of original data and D-MIAT-generated features yields best predictive performance.
theft package simplifies feature extraction for time series analysis in R.
problem Lack of a unified access point and methodological pipelines for feature-based time series analysis.
method theft package provides a unified framework for computing features from six open-source time series feature sets.
result theft enables comprehensive quantification and interpretation of time series structure.
A general formulation of optimization problems in which various candidate solutions may use different feature-sets is presented, encompassing supervised classification, automated program learning and other cases. A novel characterization of the concept of a "good quality feature" for such an optimization problem is pro…
Enhances water disaggregation for parallel appliances using shape features and Bayesian Discriminative Sparse Coding.
problem Accurately discriminate and disaggregate water consumption patterns from parallel appliances.
method Bayesian Discriminative Sparse Coding (BDSC-LP) with Laplace Prior, shape features, Gibbs sampling.
result Extensive experiments validate the effectiveness of the proposed model.
Paper proposes a new method for feature importance in model selection.
problem Feature importance in model selection.
method New tree-model explanation approach using Coefficient of Variation of feature weight.
result Novel feature explanation performs better than cross-validation in model selection.
The paper benchmarks saliency methods for time series predictions.
problem Benchmarking saliency methods for time series predictions.
method Compared saliency-based interpretability methods across diverse neural architectures.
result Saliency methods fail to reliably identify feature importance over time in time series data.
I consider unsupervised extensions of the fast stepwise linear regression algorithm \cite{efroymson1960multiple}. These extensions allow one to efficiently identify highly-representative feature variable subsets within a given set of jointly distributed variables. This in turn allows for the efficient dimensional reduc…
Extends Seeley's theorem for Bastiani's differential calculus in infinite dimensions.
problem Extending differential calculus results to infinite-dimensional spaces.
method Follows Seeley's approach but extends to continuous differentials and families of operators.
result Constructs families of extension operators for continuous differentials.
The paper extends Pearson correlation to multi-variables, useful for noise measurement and feature selection.
problem The standard Pearson correlation coefficient is limited to two variables and doesn't meet the needs for multi-variable analysis.
method The authors use random matrix theory to extend Pearson's correlation coefficient to an arbitrary number of variables.
result The extended correlation coefficient is useful for gauging noise and selecting features, particularly in classification.
Sharp feature-learning transitions and Bayes-optimal neural scaling laws in extensive-width networksstat.ML
Sharp feature transitions revealed in extensive-width networks.
problem Learning hierarchical features from noisy queries in large networks.
method Information-theoretic analysis and heuristic decoupling argument.
result Sequential phase transitions in feature learnability and effective width.
Paper explores deep learning features for complex emotion recognition.
problem Improving emotion recognition accuracy in complex emotions.
method Used pretrained networks (AudioSet Net, VoxCeleb Net, Deep Speech Net) and their deep layer features for emotion recognition.
result Achieved highest F1 score of 0.85 on EmoReact dataset.
Extends LIBOR market model to reduce exploding scenarios.
problem Exploding scenarios in market-consistent guarantees valuation.
method Mean-field extension of the LIBOR market model.
result Existence and uniqueness of MF-LMM proved.
FI-GNNs learn expressive node representations from sparse features.
problem Sparse and high-dimensional node features limit GNN performance.
method Plug-and-play GNN framework that highlights informative feature interactions.
result FI-GNNs learn highly expressive node representations on feature-sparse graphs.
Slow feature analysis (SFA) is an unsupervised-learning algorithm that extracts slowly varying features from a multi-dimensional time series. A supervised extension to SFA for classification and regression is graph-based SFA (GSFA). GSFA is based on the preservation of similarities, which are specified by a graph struc…
New method improves feature selection in tree-based models.
problem Previous feature selection methods in tree-based models lack sufficient regularization and sub-optimal performance.
method Developed a new gain penalization approach for tree-based models that allows for flexible feature-specific importance weights.
result The new method improves out-of-sample performance, especially with correlated features.
Paper proposes a novel unsupervised feature selection method using K-means and ADMM.
problem Finding a subset of features for high-dimensional unsupervised learning problems.
method Developed K-means Derived Unsupervised Feature Selection (K-means UFS) using ADMM to solve NP-hard optimization.
result K-means UFS outperforms baselines in feature selection for clustering.
New method selects features for sequential decision making.
problem Dynamic feature selection for instance-wise decisions.
method Latent variable model trained in a supervised manner; reasoning across stochastic latent space.
result Outperforms existing methods on various datasets.
Develops method to assess feature importance in black-box models for unconditional distribution.
problem Lack of methods to analyze feature importance in black-box models for unconditional distribution.
method Approximation method to compute feature importance curves for unconditional distribution.
result Produces sparse and faithful results, computationally efficient.
Enhances hashing for cross-modal retrieval using multi-view features.
problem Limited improvement in single-view hashing for cross-modal retrieval.
method Exploits multiple views to enrich feature information, learning discriminative hash codes.
result Superior performance compared to state-of-the-art methods on various datasets.
RAEUFS selects features from data without labels, improving robustness to outliers.
problem Feature selection in high-dimensional data, especially in the presence of outliers.
method RAEUFS uses a deep autoencoder to learn nonlinear feature representations, improving robustness to outliers.
result RAEUFS outperforms state-of-the-art UFS methods in both clean and outlier-contaminated data settings.
In this paper, a novel learning paradigm is presented to automatically identify groups of informative and correlated features from very high dimensions. Specifically, we explicitly incorporate correlation measures as constraints and then propose an efficient embedded feature selection method using recently developed cu…
RMH selects features from functional data for classification.
problem Dimensionality reduction and variable selection in functional data classification.
method Recursive maxima hunting (RMH) for identifying maxima of a relevance function.
result RMH achieves comparable or higher predictive accuracy than standard techniques.
Proposes TFCL to mitigate negative transfer in MTL by collaborating across features and tasks.
problem Negative transfer in Multi-Task Learning (MTL) due to dissimilar tasks.
method Task-Feature Collaborative Learning (TFCL) with heterogeneous block-diagonal structure regularizer and optimization method.
result Global convergence and block-diagonal structure recovery guarantees.
VAEM extends VAEs to handle mixed-type data heterogeneity.
problem Heterogeneous data with different types and marginal distributions.
method Two-stage training approach to handle mixed-type data.
result VAEM improves deep generative model performance on diverse tasks.
Feature selection is one of the most fundamental problems in machine learning. An extensive body of work on information-theoretic feature selection exists which is based on maximizing mutual information between subsets of features and class labels. Practical methods are forced to rely on approximations due to the diffi…
RAMPART ranks top-k features more accurately than existing methods.
problem Accurate ranking of important features in machine learning.
method Adaptive sequential halving strategy combined with ensembling techniques.
result RAMPART achieves the correct top-k ranking with high probability.
Introduces top- regularization for better feature selection in machine learning.
problem Limited ability of existing feature selection methods to reconcile feature representativeness and inter-correlations.
method Top- regularization, which induces a sub-architecture on the model's architecture to select informative features and model complex relationships.
result Uniform approximation error bound for top- regularization approximating high-dimensional sparse functions.
New method provides calibrated feature importance explanations for regression models.
problem Lack of uncertainty quantification in existing local explanation methods.
method Extension of Calibrated Explanations method to support regression and probabilistic regression.
result Calibrated Explanations for regression provides quantified uncertainty and robust explanations.
Paper enhances haptic signals distinguishability with boosted technique.
problem Lack of large datasets in haptics domain limits feature extraction.
method General framework for haptic signal analysis, using spectral features and boosted embedding.
result Framework needs less training data and outperforms state-of-the-art.
New method improves kernel approximation for larger datasets.
problem Efficiently approximate kernel functions for large datasets.
method Monte Carlo integration for numerical approximation of kernel functions.
result Improved convergence behavior and empirical support for better kernel estimates.
RIG extends IG to Riemannian manifolds for explainable AI.
problem Lack of explainability in AI models.
method Extension of Integrated Gradients to Riemannian manifolds.
result RIG restricts to IG in Euclidean space.
New approach uses deep generative models for inventory and pricing decisions.
problem Data-driven inventory and pricing decisions in feature-based newsvendor problems.
method Conditional deep generative models (cDGMs) to learn demand distribution and generate probabilistic forecasts.
result Effective in optimizing inventory and pricing decisions, with theoretical guarantees and real-world applications.
Quantum computing improves feature selection in machine learning.
problem Optimizing feature selection in machine learning problems.
method Formulated feature selection as a QUBO problem and compared quantum and classical methods.
result Quantum computing can outperform classical methods in feature selection, depending on data set.
TorchGAN simplifies GAN training and evaluation with a flexible framework.
problem Training and evaluating GANs can be complex and time-consuming.
method Modular design, extensibility, built-in support for popular models and metrics.
result TorchGAN achieves near-zero overhead compared to vanilla PyTorch.
Proposes a new method for feature selection in non-linear functions.
problem Feature selection for non-linear functions in high-dimensional data.
method Continuous relaxation of Bernoulli distributions to learn feature selection indicators via gradient descent.
result Demonstrates the effectiveness of the approach on synthetic and real-life applications.
modAL simplifies active learning in Python.
problem Making active learning research and practice easier.
method Clear, modular design; compatibility with scikit-learn.
result Facilitates fast prototyping and algorithm development.
Value selection reduces model size while maintaining accuracy.
problem Space efficiency in model size reduction.
method Two probabilistic methods based on information theory's metric: PVS and P + VS.
result Value selection achieves balance between accuracy and model size reduction.
We find that CNNs with BN and ReLU exhibit feature sparsity through selective feature penalization.
problem Feature sparsity in CNNs trained with specific techniques.
method Empirical study and hypothesis testing on sparsification mechanisms.
result Selective feature penalization leads to feature sparsity in CNNs, comparable to explicit pruning.
New model estimates corporate defaults using pure jump processes, capturing extreme events.
problem Estimating corporate defaults using standard diffusion models that underestimate short-term probabilities.
method Introduced pure jump processes with negative jumps only, derived formulas, calibrated parameters, and implemented practical tools.
result Models redistribute credit risk towards shorter maturities, improving short-term default probability estimates.
Simplex Random Featuresstat.ML
A new method for approximating softmax and Gaussian kernels with reduced error.
problem Approximating softmax and Gaussian kernels with low error.
method Simplex Random Features (SimRFs) and SimRFs+.
result SimRFs provide the smallest MSE among weight-independent geometrically-coupled PRF mechanisms.