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Euclidean space data projection classifier with cartesian genetic programming (CGP)

Most evolutionary based classifiers are built based on generated rules sets that categorize the data into respective classes. This research work is a preliminary work which proposes an evolutionary-based classifier using a simplified Cartesian Genetic Programming (CGP) evolutionary algorithm. Instea...

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Main Authors: WK Wong, Gopal Lenin, Tan, Terence, Ali Chekima
Format: Article
Language:English
English
Published: Penerbit UTeM 2018
Subjects:
QA71-90 Instruments and machines
Online Access:https://eprints.ums.edu.my/id/eprint/34975/1/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/34975/2/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/34975/
https://jtec.utem.edu.my/jtec/article/view/3817/2761
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spelling my.ums.eprints.349752022-11-30T00:15:09Z https://eprints.ums.edu.my/id/eprint/34975/ Euclidean space data projection classifier with cartesian genetic programming (CGP) WK Wong Gopal Lenin Tan, Terence Ali Chekima QA71-90 Instruments and machines Most evolutionary based classifiers are built based on generated rules sets that categorize the data into respective classes. This research work is a preliminary work which proposes an evolutionary-based classifier using a simplified Cartesian Genetic Programming (CGP) evolutionary algorithm. Instead on using evolutionary generated rule sets, the CGP generates i) a reference coordinate ii) projection functions to project data into a new 3 Dimensional Euclidean space. Subsequently, a distance boundary function of the new projected data to the reference coordinates is applied to classify the data into their respective classes. The evolutionary algorithm is based on a simplified CGP Algorithm using a 1+4 evolutionary strategy. The data projection functions were evolved using CGP for 1000 generations before stopping to extract the best functions. The Classifier was tested using three PROBEN 1 benchmarking datasets which are the PIMA Indians diabetes dataset, Heart Disease dataset and Wisconsin Breast Cancer (WBC) Dataset based on 10 fold cross validation dataset partitioning. Testing results showed that data projection function generated competitive results classification rates: Cancer dataset (97.71%), PIMA Indians dataset (77.92%) and heart disease (85.86%). Penerbit UTeM 2018 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/34975/1/FULL%20TEXT.pdf text en https://eprints.ums.edu.my/id/eprint/34975/2/ABSTRACT.pdf WK Wong and Gopal Lenin and Tan, Terence and Ali Chekima (2018) Euclidean space data projection classifier with cartesian genetic programming (CGP). Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10 (12). pp. 9-12. ISSN 2180-1843 (P-ISSN) , 2289-8131 (E-ISSN) https://jtec.utem.edu.my/jtec/article/view/3817/2761
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic QA71-90 Instruments and machines
spellingShingle QA71-90 Instruments and machines
WK Wong
Gopal Lenin
Tan, Terence
Ali Chekima
Euclidean space data projection classifier with cartesian genetic programming (CGP)
description Most evolutionary based classifiers are built based on generated rules sets that categorize the data into respective classes. This research work is a preliminary work which proposes an evolutionary-based classifier using a simplified Cartesian Genetic Programming (CGP) evolutionary algorithm. Instead on using evolutionary generated rule sets, the CGP generates i) a reference coordinate ii) projection functions to project data into a new 3 Dimensional Euclidean space. Subsequently, a distance boundary function of the new projected data to the reference coordinates is applied to classify the data into their respective classes. The evolutionary algorithm is based on a simplified CGP Algorithm using a 1+4 evolutionary strategy. The data projection functions were evolved using CGP for 1000 generations before stopping to extract the best functions. The Classifier was tested using three PROBEN 1 benchmarking datasets which are the PIMA Indians diabetes dataset, Heart Disease dataset and Wisconsin Breast Cancer (WBC) Dataset based on 10 fold cross validation dataset partitioning. Testing results showed that data projection function generated competitive results classification rates: Cancer dataset (97.71%), PIMA Indians dataset (77.92%) and heart disease (85.86%).
format Article
author WK Wong
Gopal Lenin
Tan, Terence
Ali Chekima
author_facet WK Wong
Gopal Lenin
Tan, Terence
Ali Chekima
author_sort WK Wong
title Euclidean space data projection classifier with cartesian genetic programming (CGP)
title_short Euclidean space data projection classifier with cartesian genetic programming (CGP)
title_full Euclidean space data projection classifier with cartesian genetic programming (CGP)
title_fullStr Euclidean space data projection classifier with cartesian genetic programming (CGP)
title_full_unstemmed Euclidean space data projection classifier with cartesian genetic programming (CGP)
title_sort euclidean space data projection classifier with cartesian genetic programming (cgp)
publisher Penerbit UTeM
publishDate 2018
url https://eprints.ums.edu.my/id/eprint/34975/1/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/34975/2/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/34975/
https://jtec.utem.edu.my/jtec/article/view/3817/2761
_version_ 1760231367193395200
score 13.211869

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