Parallel GEP Ensemble for Classifying Big Datasets

The paper investigates a Gene Expression Programming (GEP)-based ensemble classifier constructed using the stacked generalization concept. The classifier has been implemented with a view to enable parallel processing with the use of Spark and SWIM — an open source genetic programming library. The classifier has been validated in computational experiments carried out on benchmark datasets. Also, it has been inbvestigated how the results are influenced by some settings. The paper is an extension of a previous paper of the authors.

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Implementing Gene Expression Programming in the Parallel Environment for Big Datasets’ Classification

Jȩdrzejowicz

Jędrzejowicz

Wierzbowska

2019

Vietnam J. Comp. Sci.

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Parallel GEP Ensemble for Classifying Big Datasets

Cited by 1 publication

References 11 publications

Implementing Gene Expression Programming in the Parallel Environment for Big Datasets’ Classification

Implementing Gene Expression Programming in the Parallel Environment for Big Datasets’ Classification

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