Hybrid feature selection and peptide binding affinity prediction using an EDA based algorithm

Shelke, Kalpesh; Jayaraman, S.; Ghosh, Shameek; Valadi, Jayaraman

doi:10.1109/cec.2013.6557854

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…Estimation of Distribution Algorithms (EDAs) [7] are a type of EA which explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. EDAs have been applied to several types of machine learning tasks, including feature selection [14], [15], [18] and classification [17].…”

Section: Estimation Of Distribution Algorithmsmentioning

confidence: 99%

A Novel Evolutionary Algorithm for Automated Machine Learning Focusing on Classifier Ensembles

Xavier-Júnior

Freitas

Feitosa-Neto

et al. 2018

2018 7th Brazilian Conference on Intelligent Systems (BRACIS)

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Section: Estimation Of Distribution Algorithmsmentioning

confidence: 99%

A Novel Evolutionary Algorithm for Automated Machine Learning Focusing on Classifier Ensembles

Xavier-Júnior

Freitas

Feitosa-Neto

et al. 2018

2018 7th Brazilian Conference on Intelligent Systems (BRACIS)

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“…Other EC techniques have also been applied to feature selection, mainly including LCSs, ES, ABC, AISs, GSAs, EDAs, TS, and SA. Some of them were combined with other EC techniques [38], [53], [139] while most were applied individually to address feature selection problems [232], [54], [56], [79], [152], [220], [221], [222], [224], [225], [226]. Almost all of them are wrapper based methods.…”

Section: E Other Ec Techniques For Feature Selectionmentioning

confidence: 99%

A Survey on Evolutionary Computation Approaches to Feature Selection

Xue

Zhang

Browne

et al. 2016

IEEE Trans. Evol. Computat.

1,442

635

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“…EDAs are a type of EA which explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions [7]. EDAs have been applied to several types of machine learning tasks, including classification [17] and feature selection [14], [15], [18].…”

Section: Estimation Of Distribution Algorithmsmentioning

confidence: 99%

An evolutionary algorithm for automated machine learning focusing on classifier ensembles: An improved algorithm and extended results

Xavier-Júnior

Freitas

Ludermir

et al. 2020

Theoretical Computer Science

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Hybrid feature selection and peptide binding affinity prediction using an EDA based algorithm

Cited by 8 publications

References 35 publications

A Novel Evolutionary Algorithm for Automated Machine Learning Focusing on Classifier Ensembles

A Novel Evolutionary Algorithm for Automated Machine Learning Focusing on Classifier Ensembles

A Survey on Evolutionary Computation Approaches to Feature Selection

An evolutionary algorithm for automated machine learning focusing on classifier ensembles: An improved algorithm and extended results

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