OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models

Abstract Classification problems from different domains vary in complexity, size, and imbalance of the number of samples from different classes. Although several classification models have been proposed, selecting the right model and parameters for a given classification task to achieve good perform...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Arturo Magana-Mora, Vladimir B. Bajic
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/364ca4f44c504fb6a0a83037a7d7273f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:364ca4f44c504fb6a0a83037a7d7273f
record_format	dspace
spelling	oai:doaj.org-article:364ca4f44c504fb6a0a83037a7d7273f2021-12-02T11:52:34ZOmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models10.1038/s41598-017-04281-92045-2322https://doaj.org/article/364ca4f44c504fb6a0a83037a7d7273f2017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-04281-9https://doaj.org/toc/2045-2322Abstract Classification problems from different domains vary in complexity, size, and imbalance of the number of samples from different classes. Although several classification models have been proposed, selecting the right model and parameters for a given classification task to achieve good performance is not trivial. Therefore, there is a constant interest in developing novel robust and efficient models suitable for a great variety of data. Here, we propose OmniGA, a framework for the optimization of omnivariate decision trees based on a parallel genetic algorithm, coupled with deep learning structure and ensemble learning methods. The performance of the OmniGA framework is evaluated on 12 different datasets taken mainly from biomedical problems and compared with the results obtained by several robust and commonly used machine-learning models with optimized parameters. The results show that OmniGA systematically outperformed these models for all the considered datasets, reducing the F1 score error in the range from 100% to 2.25%, compared to the best performing model. This demonstrates that OmniGA produces robust models with improved performance. OmniGA code and datasets are available at www.cbrc.kaust.edu.sa/omniga/.Arturo Magana-MoraVladimir B. BajicNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Arturo Magana-Mora Vladimir B. Bajic OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models
description	Abstract Classification problems from different domains vary in complexity, size, and imbalance of the number of samples from different classes. Although several classification models have been proposed, selecting the right model and parameters for a given classification task to achieve good performance is not trivial. Therefore, there is a constant interest in developing novel robust and efficient models suitable for a great variety of data. Here, we propose OmniGA, a framework for the optimization of omnivariate decision trees based on a parallel genetic algorithm, coupled with deep learning structure and ensemble learning methods. The performance of the OmniGA framework is evaluated on 12 different datasets taken mainly from biomedical problems and compared with the results obtained by several robust and commonly used machine-learning models with optimized parameters. The results show that OmniGA systematically outperformed these models for all the considered datasets, reducing the F1 score error in the range from 100% to 2.25%, compared to the best performing model. This demonstrates that OmniGA produces robust models with improved performance. OmniGA code and datasets are available at www.cbrc.kaust.edu.sa/omniga/.
format	article
author	Arturo Magana-Mora Vladimir B. Bajic
author_facet	Arturo Magana-Mora Vladimir B. Bajic
author_sort	Arturo Magana-Mora
title	OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models
title_short	OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models
title_full	OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models
title_fullStr	OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models
title_full_unstemmed	OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models
title_sort	omniga: optimized omnivariate decision trees for generalizable classification models
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/364ca4f44c504fb6a0a83037a7d7273f
work_keys_str_mv	AT arturomaganamora omnigaoptimizedomnivariatedecisiontreesforgeneralizableclassificationmodels AT vladimirbbajic omnigaoptimizedomnivariatedecisiontreesforgeneralizableclassificationmodels
_version_	1718394976889470976

OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models

Ejemplares similares