DiCFS improves CFS for big data, handling large datasets efficiently.
problem Efficient feature selection for large datasets in big data.
method Distributed CFS (DiCFS) using Apache Spark for scalability and parallel processing.
result DiCFS outperforms WEKA in terms of time-efficiency and scalability.
Paper evaluates and improves private feature selection methods.
problem Feature selection in high-dimensional datasets with privacy constraints.
method Correlations-based order statistic privatized for feature selection.
result Our method significantly outperforms established baseline for private feature selection.
Fast feature selection for SHM using canonical correlation.
problem Feature selection for structural health monitoring.
method Greedy search of sum of squared canonical correlation coefficients.
result Extremely fast feature selection with good performance.
This thesis uses predictive models to forecast football injuries.
problem Understanding and predicting football injuries in athletes.
method The study employed machine learning algorithms, feature selection, and exposure records to predict injuries.
result The models accurately predicted injury recovery times, training hours, and fatigue indicators.
Approach to detect emotion from speech using majority voting and selected features.
problem Detecting human emotion from speech.
method Majority voting technique over machine learning models (NN, DT, SVM, KNN).
result Majority voting technique achieves better accuracy than individual models.
We investigate the trading behavior of Finnish individual investors trading the stocks selected to compute the OMXH25 index in 2003 by tracking the individual daily investment decisions. We verify that the set of investors is a highly heterogeneous system under many aspects. We introduce a correlation based method that…
A method for MRI brain tumor segmentation using feature vectors and kernel dictionary learning.
problem Segmenting brain tumor regions in MRI images.
method Extracting feature vectors, training kernel dictionaries, and selecting informative feature vectors.
result The method outperforms other methods in segmentation accuracy and reduces training time.
CaLoNet integrates spatial and local correlations for multivariate time series classification.
problem Ignoring spatial and local correlations in multivariate time series classification.
method Model spatial correlations using causality modeling, extract local correlations, integrate into graph neural network.
result Competitive performance compared to state-of-the-art methods on UEA datasets.
Model forecasts market structure from financial networks using machine learning.
problem Predicting market correlation structure from financial networks.
method Dynamic Asset Graph (DAG), Dynamic Minimal Spanning Tree (DMST), Dynamic Threshold Networks (DTN).
result Model improves market structure forecasting by up to 40% over benchmarks.
Review of correlation-based financial networks and entropy measures.
problem Understanding the dynamics of financial markets through correlation networks.
method Analysis of empirical correlation matrices and entropy measures.
result Entropy measures help in continuous monitoring of financial networks.
AEC technique monitors and predicts machine bearing health.
problem Monitoring and predicting the health of machine bearings.
method Sparse auto-encoder extracts features, correlation analysis identifies degradation.
result AEC technique generalizes well in run-to-failure tests.
Extends variable screening for ultrahigh-dimensional models, reducing dimensionality to sample size.
problem Statistical inference challenges in ultrahigh-dimensional linear models.
method Extends correlation-based variable screening to arbitrary linear models and post-screening inference techniques.
result Shows a condition (screening condition) sufficient for successful variable screening in arbitrary linear models.
Study uses machine learning to identify IBD biomarkers from gut microbiota.
problem Identifying biomarkers for Inflammatory Bowel Disease (IBD) from gut microbiota.
method Ensemble feature selection methods (CMIM, FCBF, mRMR, XGBoost) applied to IBD-associated metagenomics dataset.
result XGBoost minimizes microbiota used for IBD diagnosis, improving classification accuracy.
The paper uses distance correlation for brain connectivity and a novel multi-task learning model for age prediction.
problem Estimating age-related gender differences in brain functional connectivity.
method Estimates functional connectivity using distance correlation and proposes a non-convex multi-task learning model.
result The proposed non-convex multi-task learning model outperforms other models in age prediction and gender-specific connectivity.
Improves tabular data augmentation for contrastive learning.
problem Ineffective augmentation techniques for tabular data.
method Class-conditioned and feature-correlation based augmentation.
result Consistently outperforms conventional corruption methods.
Motivated by an abstract notion of low-level edge detector filters, we propose a simple method of unsupervised feature construction based on pairwise statistics of features. In the first step, we construct neighborhoods of features by regrouping features that correlate. Then we use these subsets as filters to produce n…
We investigate the planar maximally filtered graphs of the portfolio of the 300 most capitalized stocks traded at the New York Stock Exchange during the time period 2001-2003. Topological properties such as the average length of shortest paths, the betweenness and the degree are computed on different planar maximally f…
We investigate the daily correlation present among market indices of stock exchanges located all over the world in the time period Jan 1996 - Jul 2009. We discover that the correlation among market indices presents both a fast and a slow dynamics. The slow dynamics reflects the development and consolidation of globaliz…
A new feature screening method using projection correlation and knockoffs controls FDR in high-dimensional data.
problem Feature selection in ultra-high dimensional datasets with heavy-tailed errors and multivariate responses.
method Projection correlation for dependence measurement, knockoffs for FDR control, two-step approach.
result The method controls FDR and ensures sure screening under weak assumptions.
New method computes dense partial correlations with applications in graph theory and uncertainty quantification.
problem Sparse inverse covariance matrices are popular but dense solutions are overlooked.
method Derives approach based on inverse problem theory.
result New insights and approaches for model selection and data preprocessing.
Variable selection is a challenging issue in statistical applications when the number of predictors p far exceeds the number of observations n. In this ultra-high dimensional setting, the sure independence screening (SIS) procedure was introduced to significantly reduce the dimensionality by preserving the true mod…
Proposes Causal Loss to improve machine learning models' causal inference.
problem Machine learning algorithms often fail to capture causal relationships when data is inconsistent.
method Introduces Causal Loss, a model-agnostic loss function that enhances interventional capabilities.
result Causal Loss improves non-causal associative models to have interventional capabilities.
PLD distills knowledge using choice-theoretic Plackett-Luce model.
problem Model compression and knowledge transfer between large and small networks.
method PLD uses a weighted list-wise ranking loss based on the Plackett-Luce model.
result PLD achieves consistent gains across diverse architectures and distillation methods.
Causal methods for GRN inference from single-cell data often fail in real-world benchmarks.
problem Understanding when and why causal methods for GRN inference from single-cell data fail in real-world benchmarks.
method Introduced a controlled diagnostic framework to isolate and measure seven pathologies.
result Causal methods dominate in clean and structurally favorable regimes but fail in specific pathologies.
This work explains how maximizing latent correlations across multiple data views helps in identifying shared and private components.
problem Understanding how to identify shared and private components in multiview data.
method An intuitive generative model of multiview data is adopted, and latent correlation maximization is shown to guarantee the extraction of shared components.
result Latent correlation maximization guarantees the extraction of shared components across views and disentangles private information.
Temporal network analysis reveals stock market instability and new portfolio optimization tools.
problem Detecting market instability in stock markets using temporal network analysis.
method Utilized temporal network framework to characterize stock market correlation networks and employed temporal centrality as a portfolio selection tool.
result Peripheral stocks with low temporal centrality scores perform better in portfolio optimization under different schemes.
Proposes a Bayesian model for variable clustering with Gaussian graphical models to handle noise.
problem Noise in partial correlations can affect variable clustering results.
method Develops a Bayesian model that accounts for small but not zero partial correlations, evaluates using marginal likelihood.
result The proposed method is more accurate than BIC in noisy settings and provides more sensible clustering results.
Develops an online group feature selection method considering feature stream structure.
problem Online feature selection ignoring feature group structure.
method Formulates online group feature selection problem; develops OGFS method with intra-group and inter-group selection stages.
result Our method outperforms state-of-the-art methods in multiple tasks.
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.
UniFeat is an open-source Java tool for feature selection.
problem Efficient feature selection in various research areas.
method Provides a set of advanced feature selection methods.
result Facilitates rapid development of new feature selection algorithms.
AEFS selects features from high-dimensional data using autoencoders.
problem Feature selection for high-dimensional data in computer vision and machine learning.
method Combines autoencoder regression and group lasso for unsupervised feature selection.
result AEFS selects more important features than traditional methods, including linear and nonlinear information.
Gradient Boosted Feature Selection (GBFS) selects features reliably and scales well.
problem Feature selection in complex datasets.
method Gradient Boosted Trees modification.
result GBFS outperforms other feature selection algorithms on real-world data.
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.
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.
GOLFS selects features for clustering by combining global and local information.
problem Feature selection for high-dimensional clustering without labels.
method Combines global and local information via manifold learning and regularized self-representation.
result Improves feature selection and clustering accuracy.
NGP selects N features from P using neural networks in a greedy, iterative process.
problem Feature selection for non-linear prediction problems.
method Neural Greedy Pursuit (NGP) algorithm, selecting features sequentially in an iterative loss minimization procedure.
result NGP provides better performance than DeepLIFT and Drop-one-out loss methods.
We study the dynamics of the linear and non-linear serial dependencies in financial time series in a rolling window framework. In particular, we focus on the detection of episodes of statistically significant two- and three-point correlations in the returns of several leading currency exchange rates that could offer so…
A novel online feature selection method using DPP for diversity.
problem Online feature selection for diverse feature sets.
method DPP-based framework with three stages: sampling, local criteria, and global criteria.
result Demonstrated better compactness and comparable/outsuperior performance.
Beam search improves feature selection for better model performance.
problem Improving feature selection for better model performance.
method Proposed beam search as a generalization of forward selection for feature selection.
result Beam search can outperform forward selection, especially with correlated features.
Simplified feature selection using a single agent with restructured choice strategy.
problem Efficiency and cost issues in multi-agent reinforced feature selection.
method Single-agent approach with restructured choice strategy, including scanning method, feature prioritization, state representation, and reward scheme.
result Improved efficiency and effectiveness of feature selection.
A new feature selection method using structural correlation between samples.
problem Feature selection in high-dimensional data overlooks structural correlation information.
method Converts features into graph representations, uses fused lasso for feature selection.
result Demonstrates effectiveness of the proposed approach through experiments.
This paper reviews methods for feature selection and extraction in pattern analysis.
problem Complex raw data require feature selection or extraction for better discrimination or representation.
method Reviews different methods of feature selection and extraction.
result Compares various methods of feature selection and extraction.
Paper proposes a statistical test for feature selection pipelines using selective inference.
problem Assessing the significance of feature selection pipelines in data analysis.
method Selective inference technique applied to feature selection pipelines composed of various algorithms.
result The proposed statistical test controls false positive feature selection probabilities.
Active feature selection uses mutual information to choose fewer labels for better feature selection.
problem Selecting features with limited labeled data.
method Uses active feature selection with mutual information criterion, optimizing label selection for better feature quality.
result Algorithm selects features with higher mutual information using fewer labels than the data set size.
A stability metric compares feature selection algorithms in machine learning.
problem Stability of feature selection algorithms in machine learning.
method Rank-based instability index to compare MDA, LIME, and SHAP algorithms.
result LIME and SHAP are more stable than MDA, with LIME being best for human interpretability.
New method selects features for big data efficiently.
problem Feature selection challenges in huge data.
method Minipatch learning with STAMPS and AdaSTAMPS.
result AdaSTAMPS outperforms other methods in accuracy and speed.
AMBER method selects features efficiently using autoencoders and model-based elimination.
problem Efficiently selecting relevant features for classification.
method Greedy backward elimination using a ranker model and autoencoders.
result AMBER outperforms other feature selection methods in classification accuracy.
SURI boosts features with high unique relevant information for better health data analysis.
problem Preserving interpretability in health data analysis.
method Mutual information-based feature selection (MIBFS) method called SURI.
result SURI selects more relevant features leading to higher classification performance.