An incomplete dominance genetic algorithm approach to microarray data analysis

Melita, Nicolae Teodor; Holban, Ştefan

doi:10.1109/iccp.2016.7737137

Cited by 2 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this type of data, the number of samples is significantly lower than the number of features and the utilization of cross validation techniques is necessary for reliable results. The diploid GAs offer better performance than the haploid implementations for selecting features in a cross validation scenario in general, and for microarray data (Melita and Holban, 2016b) in particular.…”

Section: Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

dGAselID: An R Package for Selecting a Variable Number of Features in High Dimensional Data

Melita¹,

Holban²

2017

The R Journal

Self Cite

View full text Add to dashboard Cite

The dGAselID package proposes an original approach to feature selection in high dimensional data. The method is built upon a diploid genetic algorithm. The genotype to phenotype mapping is modeled after the Incomplete Dominance Inheritance, overpassing the necessity to define a dominance scheme. The fitness evaluation is done by user selectable supervised classifiers, from a broad range of options. Cross validation options are also accessible. A new approach to crossover, inspired from the random assortment of chromosomes during meiosis is included. Several mutation operators, inspired from genetics, are also proposed. The package is fully compatible with the data formats used in Bioconductor and MLInterfaces package, readily applicable to microarray studies, but is flexible to other feature selection applications from high dimensional data. Several options for the visualization of evolution and outcomes are implemented to facilitate the interpretation of results. The package's functionality is illustrated by examples. Different GA implementations are available as contributors' packages in R. Implementations of GAs for both floating-point and binary chromosomes are included in the genalg (Willighagen and Ballings, 2015) package. The GA (Scrucca, 2016), nsga2R (Tsou, 2015), and gaoptim (Tenorio, 2013) packages are dedicated to optimizations using GAs. A GA designed for determining training populations (Akdemir et al., 2015) is offered in the STPGA package. The package kofnGA (Wolters, 2015) aims to select a fixed-size set of integers. Variable selection applications of GAs are proposed in the

show abstract

Section: Algorithmmentioning

confidence: 99%

“…The Incomplete Dominance inheritance is an alternative to genotype to phenotype dominance schemes (Melita and Holban, 2016b) in genetic algorithms and is the approach adopted in our algorithm. Moreover, this method does not require an explicit scheme for genotype-phenotype mapping.…”

Section: Algorithmmentioning

confidence: 99%

dGAselID: An R Package for Selecting a Variable Number of Features in High Dimensional Data

Melita¹,

Holban²

2017

The R Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…Unlike filter methods, wrapper methods consider the correlation between features while selecting a subset. Differential evolution (DE; Storn & Price, 1997), ant colony optimization (ACO; Fahrudin et al, 2017), particle swarm optimization (PSO; Eberhart & Kennedy, 1995), whale optimization algorithm (WOA; Hussien et al, 2019), and genetic algorithm (GA; Melita & Holban, 2016) are some prominent examples of wrapper FS techniques. Generally, wrapper methods provide better classification accuracy than the filter method.…”

Section: Introductionmentioning

confidence: 99%