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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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10 results for “ReliefF”

Distributed ReliefF based Feature Selection in Sparkcs.LG

A distributed version of ReliefF improves scalability for large datasets.

problem Scalability of traditional FS algorithms on large datasets.

method Distributed implementation of ReliefF on Spark.

result DiReliefF outperforms non-distributed version in processing large datasets.

Feature selection Spark ReliefF Big Data Scalability

2018-11-01abs ↗pdf ↗

Classifying the Valence of Autobiographical Memories from fMRI Datacs.LG

fMRI analysis classifies autobiographical memory valence across individuals.

problem Classifying valence of autobiographical memories across different participants.

method Feature selection (ReliefF) combined with boosting methods applied to voxel space data.

result Classification accuracy of 62% in cross-participant setting, significantly higher than previous results.

fMRI autobiographical memories valence classification machine learning voxel space

2019-09-10abs ↗pdf ↗

A Copula Based Supervised Filter for Feature Selection in Diabetes Risk Prediction Using Machine Learningstat.ML

Proposes a copula-based filter for diabetes risk prediction.

problem Feature selection for robust and interpretable predictive modeling in medicine, especially for extreme patient strata.

method Copula-based supervised filter using Gumbel-copula implied upper-tail concordance score (lambda U).

result The proposed filter outperforms standard filters and provides clinically coherent predictors.

copula feature selection diabetes risk prediction machine learning upper-tail concordance

2025-05-28abs ↗pdf ↗

Performance Optimization of a Fuzzy Entropy based Feature Selection and Classification Frameworkcs.LG

Optimized fuzzy entropy framework improves feature selection and classification performance.

problem Improving feature selection and classification in fuzzy entropy frameworks.

method Implemented and compared combinations of ideal vectors, maximal similarity classifiers, and fuzzy entropy functions.

result Optimized combination of ideal vector, similarity classifier, and fuzzy entropy function achieved the most stable performance for all three datasets.

fuzzy entropy feature selection classification optimization ideal vector

2020-05-11abs ↗pdf ↗

Feature Importance Estimation with Self-Attention Networkscs.LG

SANs estimate feature importance in neural models, identifying interactions and high-ranked features.

problem Understanding and interpreting black-box neural network models.

method Self-Attention Network (SAN) architecture for feature importance estimation.

result SANs identify similar high-ranked features and feature interactions as other methods, improving predictive performance.

feature importance attention mechanism neural networks model interpretation black-box models

2020-02-11abs ↗pdf ↗

Relief-Based Feature Selection: Introduction and Reviewcs.DS

Feature selection plays a critical role in biomedical data mining, driven by increasing feature dimensionality in target problems and growing interest in advanced but computationally expensive methodologies able to model complex associations. Specifically, there is a need for feature selection methods that are computat…

2017-11-22abs ↗pdf ↗

Analysis of ensemble feature selection for correlated high-dimensional RNA-Seq cancer dataq-bio.GN

Study compares single vs ensemble feature selection for cancer diagnosis.

problem Identifying relevant variables for cancer diagnosis and prognosis.

method Comparison of single feature selection algorithms and ensemble of diverse algorithms.

result Ensemble approach did not improve predictive performance over individual algorithms.

RNA-Seq cancer feature selection ensemble machine learning

2020-04-28abs ↗pdf ↗

A study of resting-state EEG biomarkers for depression recognitioneess.SP

Study finds PLI functional connectivity feature superior for depression recognition.

problem Effective detection of depression remains a public health challenge.

method Resting state EEG data collected from MDD and normal controls; various feature types and selection methods evaluated.

result PLI functional connectivity feature superior to linear and nonlinear features; highest classification accuracy 82.31%.

depression EEG resting state functional connectivity PLI

2020-02-23abs ↗pdf ↗

Emotion Recognition in Low-Resource Settings: An Evaluation of Automatic Feature Selection Methodscs.LG

Study evaluates feature selection methods for emotion recognition in resource-constrained settings.

problem Reducing memory and computational requirements for emotion recognition in low-resource settings.

method Evaluation of three feature selection methods: ILFS, ReliefF, Fisher, and AFS.

result Smaller feature sets can achieve similar or better accuracy, reducing resource usage.

emotion recognition feature selection resource-constrained ambient intelligence health monitoring

2019-08-28abs ↗pdf ↗

Interpretable feature subset selection: A Shapley value based approachstat.ML

A new method uses Shapley values to select important features for classification.

problem Feature selection for improving classification models.

method Classification game with Shapley value apportioning of hinge loss.

result Threshold 0 on SVEA value identifies significant features.

feature selection Shapley value classification game hinge loss Bayes classifier

2020-01-12abs ↗pdf ↗