WERFE: A Gene Selection Algorithm Based on Recursive Feature Elimination and Ensemble Strategy

Chen, Qi; Meng, Zhaopeng; Su, Ran

doi:10.3389/fbioe.2020.00496

Cited by 30 publications

(15 citation statements)

References 51 publications

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“…In order to select the optimal combination of evaluated classifiers for majority vote, we implemented an exhaustive search using recursive elimination method ( Chatterjee, Dey, & Munshi, 2019 ; Q. Chen, Meng, & Su, 2020 ). Initially, the method starts with all evaluated classifiers, according to the selection criteria, it iteratively eliminates the classifiers until all possible combinations exhausted.…”

Section: Methodsmentioning

confidence: 99%

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

Chandra

Verma

Singh

et al. 2021

Expert Systems with Applications

241

190

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Highlights Proposed automatic COVID screening (ACoS) system for detection of infected patients. Random image augmentation is applied to incorporate the variability in the images. Applied hierarchical (two phase) classification to segregate three classes. Majority vote based classifier ensemble is used to combine model’s prediction. Proposed method show promising potential to detect nCOVID-19 infected patients.

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Section: Methodsmentioning

confidence: 99%

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

Chandra

Verma

Singh

et al. 2021

Expert Systems with Applications

241

190

View full text Add to dashboard Cite

show abstract

“…Both multiple voters and soft ensembles produced similar results. Chen, Meng, and Su [19] have discussed the Gene selection algorithm for small data editing problems. Well-chosen genetic selection of the algorithm should select a set of genes that achieve the highest performance and size, and for this, the genetic set should be as small as possible.…”

Section: Related Workmentioning

confidence: 99%

Design of a Novel Ensemble Model of Classification Technique for Gene-Expression Data of Lung Cancer with Modified Genetic Algorithm

Chandrakar¹,

Shrivas²,

Sahu³

2021

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Gene expression levels are important for identifying and diagnosing diseases like cancer. Gene expression microarray information contains a high extent feature set, which minimizes the performance and the accuracy of classifiers. OBJECTIVES: This paper proposes a Modified Genetic Algorithm (MGA) that is based on Classifier Subset Evaluators -Genetic Search (Eval-CSE_GS) for selecting the relevant feature subsets. The MGA feature selection procedure is applied to microarray information for cancer patients that minimize a high dimension feature subset into low dimension feature subsets. METHODS: The various data mining methods for classifying the various kinds of cancer disease patients are presented. The proposed model refers to an ensemble model (PEM) for the organization of cancer disease by reducing the feature subsets, which results show improvements in the success rate. RESULTS: The proposed ensemble model obtains the accuracy of 94.58%, 96.56% and 97.04% for PEM-1 to PEM-3, respectively. CONCLUSION: Our proposed MGA-PEM model gives satisfactory results for cancer identification and classification.

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“…Considering the Tree algorithm, we carried out cross-validation to prune the Tree with minimum deviance. Also, we applied the RecursiveF eatureElimination [38] to implement gene selection with NB. The parameter settings of the other algorithms were the same as the classification part.…”

Section: Evaluation Of Gene Selectionmentioning

confidence: 99%

A Systematic Evaluation of Methods for Cell Phenotype Classification Using Single-Cell RNA Sequencing Data

Cao

Xing

Majd

et al. 2021

Preprint

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Background: Single-cell RNA sequencing (scRNA-seq) yields valuable insights about gene expression and gives critical information about complex tissue cellular composition. In the analysis of single-cell RNA sequencing, the annotations of cell subtypes are often done manually, which is time-consuming and irreproducible. Garnett is a cell-type annotation software based the on elastic net method. Beside cell-type annotation, supervised machine learning methods can also be applied to predict other cell phenotypes from genomic data. Despite the popularity of such applications, there is no existing study to systematically investigate the performance of those supervised algorithms in various sizes of scRNA-seq data sets. Methods and Results: This study evaluates 13 popular supervised machine learning algorithms to classify cell phenotypes, using published real and simulated data sets with diverse cell sizes. The benchmark contained two parts. In the first part, we used real data sets to assess the popular supervised algorithms’ computing speed and cell phenotype classification performance. The classification performances were evaluated using AUC statistics, F1-score, precision, recall, and false-positive rate. In the second part, we evaluated gene selection performance using published simulated data sets with a known list of real genes. Conclusion: The study outcomes showed that ElasticNet with interactions performed best in small and medium data sets. NB was another appropriate method for medium data sets. In large data sets, XGB works excellent. Ensemble algorithms were not significantly superior to individual machine learning methods. Adding interactions to ElasticNet can help, and the improvement was significant in small data sets.

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WERFE: A Gene Selection Algorithm Based on Recursive Feature Elimination and Ensemble Strategy

Cited by 30 publications

References 51 publications

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

Design of a Novel Ensemble Model of Classification Technique for Gene-Expression Data of Lung Cancer with Modified Genetic Algorithm

A Systematic Evaluation of Methods for Cell Phenotype Classification Using Single-Cell RNA Sequencing Data

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