@article {1045,
	title = {T{\'e}cnicas de ajuste de hiperpar{\'a}metros de algoritmos de aprendizaje autom{\'a}tico para la estimaci{\'o}n de esfuerzo: un mapeo de literatura},
	journal = {Revista Ib{\'e}rica de Sistemas e Tecnologias de Informa{\c c}{\~a}o},
	year = {2021},
	month = {02},
	pages = {305-318},
	keywords = {Algorithms, Computers{\textendash}Information Science And Information Theory, Estimation, Machine learning, Mapping, Parameter estimation, Process parameters, Software development, Tuning},
	isbn = {16469895},
	url = {https://search-proquest-com.ezproxy.sibdi.ucr.ac.cr/scholarly-journals/t{\'e}cnicas-de-ajuste-hiperpar{\'a}metros-algoritmos/docview/2493869605/se-2?accountid=28692},
	author = {Villalobos-Arias,Leonardo and Quesada-L{\'o}pez,Christian and Mart{\'\i}nez,Alexandra and Jenkins,Marcelo}
}
@article {948,
	title = {Uso de t{\'e}cnicas de miner{\'\i}a de datos y aprendizaje autom{\'a}tico para la detecci{\'o}n de fraudes en estados financieros: un mapeo sistem{\'a}tico de literatura},
	journal = {Revista Ib{\'e}rica de Sistemas e Tecnologias de Informa{\c c}{\~a}o},
	year = {2020},
	month = {04},
	pages = {97-109},
	keywords = {Algorithms, Certification testing, Computers{\textendash}Information Science And Information Theory, Data mining, Decision trees, Financial statements, Fraud, Kernels, Machine learning, Mapping},
	isbn = {16469895},
	url = {https://search-proquest-com.ezproxy.sibdi.ucr.ac.cr/docview/2388305034?accountid=28692},
	author = {Ram{\'\i}rez-Alp{\'\i}zar,Alex and Jenkins,Marcelo and Mart{\'\i}nez,Alexandra and Quesada-L{\'o}pez,Christian}
}
@article {739,
	title = {A genetic algorithm based framework for software effort prediction},
	journal = {Journal of Software Engineering Research and Development},
	volume = {5},
	year = {2017},
	month = {May},
	pages = {4},
	abstract = {<p>Several prediction models have been proposed in the literature using different techniques obtaining different results in different contexts. The need for accurate effort predictions for projects is one of the most critical and complex issues in the software industry. The automated selection and the combination of techniques in alternative ways could improve the overall accuracy of the prediction models.</p>},
	keywords = {Effort prediction model, Empirical study, function points, Genetic approach, ISBSG dataset, Learning schemes, Machine learning, Software effort estimation},
	issn = {2195-1721},
	doi = {10.1186/s40411-017-0037-x},
	url = {https://doi.org/10.1186/s40411-017-0037-x},
	author = {Murillo-morera, Juan and Quesada-L{\'o}pez, Christian and Castro-Herrera, Carlos and Jenkins, Marcelo}
}
