A genetic algorithm approach to optimising random forests applied to class engineered data

Eyad Elyan; Mohamed Medhat Gaber

doi:10.1016/j.ins.2016.08.007

A genetic algorithm approach to optimising random forests applied to class engineered data

Eyad Elyan^*, Mohamed Medhat Gaber

^*Corresponding author for this work

Robert Gordon University

Research output: Contribution to journal › Article › peer-review

71 Citations (SciVal)

Original language	English
Pages (from-to)	220-234
Number of pages	15
Journal	Information Sciences
Volume	384
DOIs	https://doi.org/10.1016/j.ins.2016.08.007
Publication status	Published (VoR) - 1 Apr 2017

Keywords

Class decomposition
Genetic algorithm
Life science
Random forests

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10.1016/j.ins.2016.08.007

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title = "A genetic algorithm approach to optimising random forests applied to class engineered data",

keywords = "Class decomposition, Genetic algorithm, Life science, Random forests",

author = "Eyad Elyan and Gaber, \{Mohamed Medhat\}",

note = "Publisher Copyright: {\textcopyright} 2016",

year = "2017",

month = apr,

day = "1",

doi = "10.1016/j.ins.2016.08.007",

language = "English",

volume = "384",

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journal = "Information Sciences",

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T1 - A genetic algorithm approach to optimising random forests applied to class engineered data

AU - Elyan, Eyad

AU - Gaber, Mohamed Medhat

PY - 2017/4/1

Y1 - 2017/4/1

KW - Class decomposition

KW - Genetic algorithm

KW - Life science

KW - Random forests

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DO - 10.1016/j.ins.2016.08.007

M3 - Article

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SN - 0020-0255

VL - 384

SP - 220

EP - 234

JO - Information Sciences

JF - Information Sciences

ER -

A genetic algorithm approach to optimising random forests applied to class engineered data

Keywords

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