An algorithm to estimate the risk of child labor

Ricky Bryan Quiñones Fabian; Ruben Aldair Andamayo Alcantara; Abel Jesus Inga Lopez; Jaime Antonio Huaytalla Pariona; Jimmy Alberth Deza Quispe

doi:10.5267/j.dsl.2022.5.004

An algorithm to estimate the risk of child labor

Ricky Bryan Quiñones Fabian, Ruben Aldair Andamayo Alcantara, Abel Jesus Inga Lopez, Jaime Antonio Huaytalla Pariona, Jimmy Alberth Deza Quispe

EAP Ingeniería mecatrónica

Producción científica: Artículo Científico › Artículo original › revisión exhaustiva

Resumen

In developing countries, child labor has become a significant problem with adverse effects in the present and future for society and individuals. There are many causes that obligate children to abandon school and start working. Economic, social, familiar, and personal problems can expel children from school, inhibiting them from living appropriately. Polls like the ENAHO in Peru tried to recollect relevant data as much as possible to explain this problem. With many variables, it is necessary to have a methodology to build an algorithm with enough explanatory power to explain the situation. Therefore, this research elaborated an algorithm through Lasso to proportionate a statistical explanation of child labor. Due to the type of data, the regression was logistic.

Idioma original	Inglés estadounidense
Páginas (desde-hasta)	521-528
-	8
Publicación	Decision Science Letters
Volumen	11
N.º	4
DOI	https://doi.org/10.5267/j.dsl.2022.5.004
Estado	Indizado - 1 set. 2022

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© 2022 by the authors; licensee Growing Science, Canada. © 2022 by the authors; distributed under the term.

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title = "An algorithm to estimate the risk of child labor",

abstract = "In developing countries, child labor has become a significant problem with adverse effects in the present and future for society and individuals. There are many causes that obligate children to abandon school and start working. Economic, social, familiar, and personal problems can expel children from school, inhibiting them from living appropriately. Polls like the ENAHO in Peru tried to recollect relevant data as much as possible to explain this problem. With many variables, it is necessary to have a methodology to build an algorithm with enough explanatory power to explain the situation. Therefore, this research elaborated an algorithm through Lasso to proportionate a statistical explanation of child labor. Due to the type of data, the regression was logistic.",

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AU - Fabian, Ricky Bryan Quiñones

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AU - Pariona, Jaime Antonio Huaytalla

AU - Quispe, Jimmy Alberth Deza

PY - 2022/9/1

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N2 - In developing countries, child labor has become a significant problem with adverse effects in the present and future for society and individuals. There are many causes that obligate children to abandon school and start working. Economic, social, familiar, and personal problems can expel children from school, inhibiting them from living appropriately. Polls like the ENAHO in Peru tried to recollect relevant data as much as possible to explain this problem. With many variables, it is necessary to have a methodology to build an algorithm with enough explanatory power to explain the situation. Therefore, this research elaborated an algorithm through Lasso to proportionate a statistical explanation of child labor. Due to the type of data, the regression was logistic.

AB - In developing countries, child labor has become a significant problem with adverse effects in the present and future for society and individuals. There are many causes that obligate children to abandon school and start working. Economic, social, familiar, and personal problems can expel children from school, inhibiting them from living appropriately. Polls like the ENAHO in Peru tried to recollect relevant data as much as possible to explain this problem. With many variables, it is necessary to have a methodology to build an algorithm with enough explanatory power to explain the situation. Therefore, this research elaborated an algorithm through Lasso to proportionate a statistical explanation of child labor. Due to the type of data, the regression was logistic.

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DO - 10.5267/j.dsl.2022.5.004

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JO - Decision Science Letters

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An algorithm to estimate the risk of child labor

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