Cargando…

Hellinger distance-based stable sparse feature selection for high-dimensional class-imbalanced data

BACKGROUND: Feature selection in class-imbalance learning has gained increasing attention in recent years due to the massive growth of high-dimensional class-imbalanced data across many scientific fields. In addition to reducing model complexity and discovering key biomarkers, feature selection is a...

Descripción completa

Detalles Bibliográficos
Autores principales:	Fu, Guang-Hui, Wu, Yuan-Jiao, Zong, Min-Jie, Pan, Jianxin
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2020
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7092448/ https://www.ncbi.nlm.nih.gov/pubmed/32293252 http://dx.doi.org/10.1186/s12859-020-3411-3

Ejemplares similares

The Use of Hellinger Distance Undersampling Model to Improve the Classification of Disease Class in Imbalanced Medical Datasets
por: Al-Shamaa, Zina Z. R., et al.
Publicado: (2020)

Retracted: The Use of Hellinger Distance Undersampling Model to Improve the Classification of Disease Class in Imbalanced Medical Datasets
por: and Biomechanics, Applied Bionics
Publicado: (2023)

Hellinger Information Matrix and Hellinger Priors
por: Shemyakin, Arkady
Publicado: (2023)

Feature Ranking and Screening for Class-Imbalanced Metabolomics Data Based on Rank Aggregation Coupled with Re-Balance
por: Fu, Guang-Hui, et al.
Publicado: (2021)

Robust Inference after Random Projections via Hellinger Distance for Location-Scale Family
por: Li, Lei, et al.
Publicado: (2019)

Rare Event Analysis for Minimum Hellinger Distance Estimators via Large Deviation Theory
por: Vidyashankar, Anand N., et al.
Publicado: (2021)

Empirical Squared Hellinger Distance Estimator and Generalizations to a Family of α-Divergence Estimators
por: Ding, Rui, et al.
Publicado: (2023)

Fine Properties of Geodesics and Geodesic [Formula: see text] -Convexity for the Hellinger–Kantorovich Distance
por: Liero, Matthias, et al.
Publicado: (2023)

Class prediction for high-dimensional class-imbalanced data
por: Blagus, Rok, et al.
Publicado: (2010)

SMOTE for high-dimensional class-imbalanced data
por: Blagus, Rok, et al.
Publicado: (2013)

Asymptotic Properties for Methods Combining the Minimum Hellinger Distance Estimate and the Bayesian Nonparametric Density Estimate
por: Wu, Yuefeng, et al.
Publicado: (2018)

A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data
por: Bommert, Andrea, et al.
Publicado: (2017)

Sparse Zero-Sum Games as Stable Functional Feature Selection
por: Sokolovska, Nataliya, et al.
Publicado: (2015)

Evolutionary Mahalanobis Distance-Based Oversampling for Multi-Class Imbalanced Data Classification
por: Yao, Leehter, et al.
Publicado: (2021)

Improved shrunken centroid classifiers for high-dimensional class-imbalanced data
por: Blagus, Rok, et al.
Publicado: (2013)

A Sparse-Modeling Based Approach for Class Specific Feature Selection
por: Nardone, Davide, et al.
Publicado: (2019)

Energy Efficient Sparse Connectivity from Imbalanced Synaptic Plasticity Rules
por: Sacramento, João, et al.
Publicado: (2015)

Iterative ensemble feature selection for multiclass classification of imbalanced microarray data
por: Yang, Junshan, et al.
Publicado: (2016)

Hybrid Feature Selection Framework for the Parkinson Imbalanced Dataset Prediction Problem
por: Qasim, Hayder Mohammed, et al.
Publicado: (2021)

Feature Selection and Pedestrian Detection Based on Sparse Representation
por: Yao, Shihong, et al.
Publicado: (2015)

Sparse feature learning for multi-class Parkinson’s disease classification
por: Lei, Haijun, et al.
Publicado: (2018)

Stable variable ranking and selection in regularized logistic regression for severely imbalanced big binary data
por: Nadeem, Khurram, et al.
Publicado: (2023)

Gene expression data analysis using Hellinger correlation in weighted gene co-expression networks (WGCNA)
por: Zhang, Tianjiao, et al.
Publicado: (2022)

Feature Selection for High-Dimensional and Imbalanced Biomedical Data Based on Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm
por: Abdulrauf Sharifai, Garba, et al.
Publicado: (2020)

A Comparison of Sparse Partial Least Squares and Elastic Net in Wavelength Selection on NIR Spectroscopy Data
por: Fu, Guang-Hui, et al.
Publicado: (2019)

Distance- and Momentum-Based Symbolic Aggregate Approximation for Highly Imbalanced Classification
por: Yang, Dong-Hyuk, et al.
Publicado: (2022)

A novel multi-module integrated intrusion detection system for high-dimensional imbalanced data
por: Cui, Jiyuan, et al.
Publicado: (2022)

Analytical Model for Early Design Stage of Cable-Stayed Suspension Bridges Based on Hellinger–Reissner Variational Method
por: Feng, Qian, et al.
Publicado: (2022)

Stabl: sparse and reliable biomarker discovery in predictive modeling of high-dimensional omic data
por: Hédou, Julien, et al.
Publicado: (2023)

Dealing With Missing, Imbalanced, and Sparse Features During the Development of a Prediction Model for Sudden Death Using Emergency Medicine Data: Machine Learning Approach
por: Chen, Xiaojie, et al.
Publicado: (2023)

Continuum mechanics through the eighteenth and nineteenth centuries: historical perspectives from John Bernoulli (1727) to Ernst Hellinger (1914)
por: Maugin, Gérard A
Publicado: (2014)

Sparse Bayesian classification and feature selection for biological expression data with high correlations
por: Yang, Xian, et al.
Publicado: (2017)

Feature Selection in High-Dimensional Models via EBIC with Energy Distance Correlation
por: Ocloo, Isaac Xoese, et al.
Publicado: (2022)

Resampling Methods Improve the Predictive Power of Modeling in Class-Imbalanced Datasets
por: Lee, Paul H.
Publicado: (2014)

Performance Analysis of Binarization Strategies for Multi-class Imbalanced Data Classification
por: Żak, Michał, et al.
Publicado: (2020)

An oversampling method for multi-class imbalanced data based on composite weights
por: Deng, Mingyang, et al.
Publicado: (2021)

Selective oversampling approach for strongly imbalanced data
por: Gnip, Peter, et al.
Publicado: (2021)

A Simultaneous Feature Selection and Compositional Association Test for Detecting Sparse Associations in High-Dimensional Metagenomic Data
por: Hinton, Andrew L., et al.
Publicado: (2022)

Peculiar Genes Selection: A new features selection method to improve classification performances in imbalanced data sets
por: Martina, Federica, et al.
Publicado: (2017)

A Novel Feature Extraction Method with Feature Selection to Identify Golgi-Resident Protein Types from Imbalanced Data
por: Yang, Runtao, et al.
Publicado: (2016)

Cannot write session to /tmp/vufind_sessions/sess_smrj9e43211kkqmgto01cq0gf9