scholarly journals Is seeding a good strategy in multi-objective feature selection when feature models evolve?

2018 ◽  
Vol 95 ◽  
pp. 266-280 ◽  
Author(s):  
Takfarinas Saber ◽  
David Brevet ◽  
Goetz Botterweck ◽  
Anthony Ventresque
Keyword(s):  
Feature Selection ◽  
Good Strategy ◽  
Feature Models ◽  
Multi Objective

scholarly journals Multi-view feature selection for identifying gene markers: a diversified biological data driven approach

2020 ◽  
Vol 21 (S18) ◽  
Author(s):  
Sudipta Acharya ◽  
Laizhong Cui ◽  
Yi Pan
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Selection ◽  
Marker Gene ◽  
Biological Data ◽  
Protein Interaction Data ◽  
Marker Genes ◽  
Data Sets ◽  
Gene Markers ◽  
Multi Objective

Abstract Background In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population. Results In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select. Three important resources of biological data (gene ontology, protein interaction data, protein sequence) along with gene expression values are collectively utilized to design two different views. UMVMO-select aims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets. Conclusion A thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting.

A Novel Outlook on Feature Selection as a Multi-objective Problem

2020 ◽  
pp. 68-81
Author(s):  
Pietro Barbiero ◽  
Evelyne Lutton ◽  
Giovanni Squillero ◽  
Alberto Tonda
Keyword(s):  
Feature Selection ◽  
Multi Objective
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