Search · four archives

Results for ensemble feature selection

or ask it as a questionAsk the stacks →

ArchivesarXiv PubMed bioRxiv chemRxiv

WhenAny time Past month Past year Past 5 years

OrderMost relevant Most cited Newest

23 papers · ranked by Valyu relevance

01
PPubMed27 Jun 2017
EFS: an ensemble feature selection tool implemented as R-package and web-application
Ursula Neumann, Nikita Genze, Dominik Heider
Background Feature selection methods aim at identifying a subset of features that improve the prediction performance of subsequent classification models and thereby also simplify their interpretability. Preceding studies demonstrated that single feature selection methods can have specific biases, whereas an ensemble…
93relevance
02
PPubMed9 Jan 2023
Ensemble feature selection with data-driven thresholding for Alzheimer's disease biomarker discovery
Annette Spooner, Gelareh Mohammadi, Perminder S. Sachdev, Henry Brodaty + 1 more
'Henry Brodaty' 'Arcot Sowmya' ''] Background Feature selection is often used to identify the important features in a dataset but can produce unstable results when applied to high-dimensional data. The stability of feature selection can be improved with the use of feature selection ensembles, which aggregate the…
92relevance
03
PPubMed18 Nov 2016
Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach
Ursula Neumann, Mona Riemenschneider, Jan-Peter Sowa, Theodor Baars + 3 more
'Julia Kälsch' 'Ali Canbay' 'Dominik Heider'] Motivation Biomarker discovery methods are essential to identify a minimal subset of features (e.g., serum markers in predictive medicine) that are relevant to develop prediction models with high accuracy. By now, there exist diverse feature selection methods, which either…
91relevance
04
PPubMed26 Apr 2022
Novel Ensemble Feature Selection Approach and Application in Repertoire Sequencing Data He et al. Novel Ensemble Feature Selection Approach
Tao He, Jason Min Baik, Chiemi Kato, Hai Yang + 3 more
The T and B cell repertoire make up the adaptive immune system and is mainly generated through somatic V(D)J gene recombination. Thus, the VJ gene usage may be a potential prognostic or predictive biomarker. However, analysis of the adaptive immune system is challenging due to the heterogeneity of the clonotypes that…
91relevance
05
PPubMed3 Apr 2020
sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and <i> t </i> Statistic
Pijush Das, Anirban Roychowdhury, Subhadeep Das, Susanta Roychoudhury + 1 more
'Susanta Roychoudhury' 'Sucheta Tripathy'] Biological data are accumulating at a faster rate, but interpreting them still remains a problem. Classifying biological data into distinct groups is the first step in understanding them. Data classification in response to a certain treatment is an extremely important aspect…
90relevance
06
AarXiv2022
Ensemble feature selection with data-driven thresholding for Alzheimer's disease biomarker discovery
Annette Spooner, Gelareh Mohammadi, Perminder S. Sachdev, Henry Brodaty + 1 more
'Henry Brodaty' 'Arcot Sowmya'] Healthcare datasets present many challenges to both machine learning and statistics as their data are typically heterogeneous, censored, high-dimensional and have missing information. Feature selection is often used to identify the important features but can produce unstable results when…
88relevance
07
AarXiv1 Nov 2019
An Ensemble Feature Selection Framework Integrating Stability
Xiaokang Zhang, Inge Jonassen
Ensemble learning that can be used to combine the predictions from multiple learners has been widely applied in pattern recognition, and has been reported to be more robust and accurate than the individual learners. This ensemble logic has recently also been more applied in feature selection. There are basically two…
87relevance
08
BbioRxiv22 Jul 2022
Hybrid Rank Aggregation (HRA): A novel rank aggregation method for ensemble-based feature selection
Rahi Jain, Wei Xu
Feature selection (FS) reduces the dimensions of high dimensional data. Among many FS approaches, ensemble-based feature selection (EFS) is one of the commonly used approaches. The rank aggregation (RA) step influences the feature selection of EFS. Currently, the EFS approach relies on using a single RA algorithm to…
87relevance
09
BbioRxiv19 Oct 2017
optSelect: using agent-based modeling and binary PSO techniques for ensemble feature selection and stability assessment
Karan Uppal, Eva K. Lee
Recent studies have shown that the ensemble feature selection approaches are essential for generating robust classifiers. Existing methods for aggregating feature lists from different methods require use of arbitrary thresholds for selecting the top ranked features and do not account for classification accuracy while…
87relevance
10
BbioRxiv22 Feb 2022
Supervised Rank aggregation (SRA): A novel rank aggregation approach for ensemble-based feature selection
Rahi Jain, Wei Xu
Feature selection (FS) is critical for high dimensional data analysis. Ensemble based feature selection (EFS) is a commonly used approach to develop FS techniques. Rank aggregation (RA) is an essential step of EFS where results from multiple models are pooled to estimate feature importance. However, the literature…
86relevance
11
PPubMed15 Jun 2016
Ensemble Feature Learning of Genomic Data Using Support Vector Machine Ensemble Feature Learning of Genomic Data
Ali Anaissi, Madhu Goyal, Daniel R. Catchpoole, Ali Braytee + 2 more
'Paul J. Kennedy' 'Bin Liu'] The identification of a subset of genes having the ability to capture the necessary information to distinguish classes of patients is crucial in bioinformatics applications. Ensemble and bagging methods have been shown to work effectively in the process of gene selection and classification.…
86relevance
12
PPubMed4 Jan 2024
An ensemble learning-based feature selection algorithm for identification of biomarkers of renal cell carcinoma
Zekun Xin, Ruhong Lv, Wei Liu, Shenghan Wang + 4 more
'Guangyu Sun' 'Xiangtao Li'] Feature selection plays a crucial role in classification tasks as part of the data preprocessing process. Effective feature selection can improve the robustness and interpretability of learning algorithms, and accelerate model learning. However, traditional statistical methods for feature…
85relevance
13
BbioRxiv18 Oct 2019
Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction
Li-Hsin Cheng, Che Lin
Breast cancer is a heterogeneous disease. In order to guide proper treatment decisions for each individual patient, there is an urgent need for robust prognostic biomarkers that allow reliable prognosis prediction. Gene feature selection on microarray data is an approach to systematically discover potential biomarkers.…
85relevance
14
AarXiv2022
Ensemble feature selection with clustering for analysis of high-dimensional, correlated clinical data in the search for Alzheimer's disease biomarkers
Annette Spooner, Gelareh Mohammadi, Perminder S. Sachdev, Henry Brodaty + 1 more
'Henry Brodaty' 'Arcot Sowmya'] Healthcare datasets often contain groups of highly correlated features, such as features from the same biological system. When feature selection is applied to these datasets to identify the most important features, the biases inherent in some multivariate feature selectors due to…
84relevance
15
AarXiv2022
AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection
Yunpu Zhao
Automated machine learning has achieved remarkable technological developments in recent years, and building an automated machine learning pipeline is now an essential task. However, existing AutoML pipeline approaches adopt monotonous ensemble strategies across different machine learning classification tasks. They…
82relevance
16
AarXiv12 Dec 2021
Feature selection for imbalanced data with deep sparse autoencoders ensemble
Michela Carlotta Massi, Francesca Gasperoni, Francesca Ieva, Anna Maria Paganoni
'Anna Maria Paganoni'] Class imbalance is a common issue in many domain applications of learning algorithms. Oftentimes, in the same domains it is much more relevant to correctly classify and profile minority class observations. This need can be addressed by Feature Selection (FS), that offers several further…
81relevance
17
BbioRxiv4 Aug 2020
SubFeat: Feature Subspacing Ensemble Classifier for Function Prediction of DNA, RNA and Protein Sequences
H.M.Fazlul Haque, Fariha Arifin, Sheikh Adilina, Muhammod Rafsanjani + 1 more
The information of a cell is primarily contained in Deoxyribonucleic Acid (DNA). There is a flow of information of DNA to protein sequences via Ribonucleic acids (RNA) through transcription and translation. These entities are vital for the genetic process. Recent developments in epigenetic also show the importance of…
73relevance
18
BbioRxiv9 Feb 2022
Unsupervised encoding selection through ensemble pruning for biomedical classification
Sebastian Spänig, Alexander Michel, Dominik Heider
Owing to the rising levels of multi-resistant pathogens, antimicrobial peptides, an alternative strategy to classic antibiotics, got more attention. A crucial part is thereby the costly identification and validation. With the ever-growing amount of annotated peptides, researchers employed artificial intelligence to…
72relevance
19
CchemRxiv14 Jul 2025
Machine Learning Analysis of Molecular Dynamics Properties Influencing Drug Solubility
Authors not listed
Solubility is critical in drug discovery and development, as it significantly influences a medication's bioavailability and therapeutic efficacy. Understanding solubility at the early stages of drug discovery is essential for minimizing resource consumption and enhancing the likelihood of clinical success via…
72relevance
20
CchemRxiv6 Oct 2025
A Target-Specific Machine Learning Framework for Predicting Fuel Blend Properties
Authors not listed
We present a multi-stage framework for predictive modeling that integrates automated feature engineering, selective dimensionality reduction, and targeted ensembling. Our pipeline begins with feature generation using a GPU-accelerated adaptation of AutoFeat, followed by variance-based pruning and LightGBM gain-based…
69relevance
21
CchemRxiv9 Jun 2025
A 2025 perspective on the role of machine learning in clinical proteomics
Authors not listed
Machine learning holds significant promise for accelerating biomarker discovery in clinical proteomics, yet its real-world impact remains limited by widespread methodological pitfalls and unrealistic expectations. In this perspective, we critically examine the integration of machine learning into clinical proteomics…
65relevance
22
CchemRxiv20 Oct 2021
Evaluating scalable supervised learning for synthesize-on-demand chemical libraries
Moayad Alnammi, Shengchao Liu, Spencer S Ericksen, Gene E Ananiev + 6 more
Traditional small molecule drug discovery is a time consuming and costly endeavor. High-throughput chemical screening can only assess a tiny fraction of drug-like chemical space. The strong predictive power of modern machine learning methods for virtual chemical screening enables training models on known active and…
64relevance
23
CchemRxiv2 Jun 2025
Increasing trustworthiness of machine learning-based drug sensitivity prediction with a multivariate random forest approach
Authors not listed
Ensuring the trustworthiness of machine learning (ML) models in high-stake applications is crucial. One such application is predicting anti-cancer drug sensitivity, where ML models are built with the final goal of integrating them into treatment recommendation systems for personalized medicine. Here, we propose a…
55relevance

Results for ensemble feature selection

EFS: an ensemble feature selection tool implemented as R-package and web-application

Ensemble feature selection with data-driven thresholding for Alzheimer's disease biomarker discovery

Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach

Novel Ensemble Feature Selection Approach and Application in Repertoire Sequencing Data He et al. Novel Ensemble Feature Selection Approach

sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and <i> t </i> Statistic

Ensemble feature selection with data-driven thresholding for Alzheimer's disease biomarker discovery

An Ensemble Feature Selection Framework Integrating Stability

Hybrid Rank Aggregation (HRA): A novel rank aggregation method for ensemble-based feature selection

optSelect: using agent-based modeling and binary PSO techniques for ensemble feature selection and stability assessment

Supervised Rank aggregation (SRA): A novel rank aggregation approach for ensemble-based feature selection

Ensemble Feature Learning of Genomic Data Using Support Vector Machine Ensemble Feature Learning of Genomic Data

An ensemble learning-based feature selection algorithm for identification of biomarkers of renal cell carcinoma

Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction

Ensemble feature selection with clustering for analysis of high-dimensional, correlated clinical data in the search for Alzheimer's disease biomarkers

AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection

Feature selection for imbalanced data with deep sparse autoencoders ensemble

SubFeat: Feature Subspacing Ensemble Classifier for Function Prediction of DNA, RNA and Protein Sequences

Unsupervised encoding selection through ensemble pruning for biomedical classification

Machine Learning Analysis of Molecular Dynamics Properties Influencing Drug Solubility

A Target-Specific Machine Learning Framework for Predicting Fuel Blend Properties

A 2025 perspective on the role of machine learning in clinical proteomics

Evaluating scalable supervised learning for synthesize-on-demand chemical libraries

Increasing trustworthiness of machine learning-based drug sensitivity prediction with a multivariate random forest approach

EFS: an ensemble feature selection tool implemented as R-package and web-application

Ensemble feature selection with data-driven thresholding for Alzheimer's disease biomarker discovery

Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach

Novel Ensemble Feature Selection Approach and Application in Repertoire Sequencing Data He et al. Novel Ensemble Feature Selection Approach

sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and <i> t </i> Statistic

Ensemble feature selection with data-driven thresholding for Alzheimer's disease biomarker discovery

An Ensemble Feature Selection Framework Integrating Stability

Hybrid Rank Aggregation (HRA): A novel rank aggregation method for ensemble-based feature selection

optSelect: using agent-based modeling and binary PSO techniques for ensemble feature selection and stability assessment

Supervised Rank aggregation (SRA): A novel rank aggregation approach for ensemble-based feature selection

Ensemble Feature Learning of Genomic Data Using Support Vector Machine Ensemble Feature Learning of Genomic Data

An ensemble learning-based feature selection algorithm for identification of biomarkers of renal cell carcinoma

Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction

Ensemble feature selection with clustering for analysis of high-dimensional, correlated clinical data in the search for Alzheimer's disease biomarkers

AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection

Feature selection for imbalanced data with deep sparse autoencoders ensemble

SubFeat: Feature Subspacing Ensemble Classifier for Function Prediction of DNA, RNA and Protein Sequences

Unsupervised encoding selection through ensemble pruning for biomedical classification

Machine Learning Analysis of Molecular Dynamics Properties Influencing Drug Solubility

A Target-Specific Machine Learning Framework for Predicting Fuel Blend Properties

A 2025 perspective on the role of machine learning in clinical proteomics

Evaluating scalable supervised learning for synthesize-on-demand chemical libraries

Increasing trustworthiness of machine learning-based drug sensitivity prediction with a multivariate random forest approach