A Framework for Selecting Machine Learning Models Using TOPSIS

Maikel Yelandi Leyva Vazquezl; Luis Andy  Briones Peñafiel; Steven Xavier  Sanchez Muñoz; Miguel Angel Quiroz Martinez

doi:10.1007/978-3-030-51328-3_18

A Framework for Selecting Machine Learning Models Using TOPSIS

Maikel Yelandi Leyva Vazquezl, Luis Andy Briones Peñafiel, Steven Xavier Sanchez Muñoz, Miguel Angel Quiroz Martinez

Grupo de Investigación en Inteligencia Artificial y Reconocimiento Facial (GIIAR)

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

10 Citas (Scopus)

Resumen

In machine learning, it is common when multiple algorithms are applied to different data sets that are complex because of their accelerated growth, a decision problem arises, i.e., how to select the algorithm with the best performance? This has generated the need to implement new information analysis techniques to support decision making. The technique of multi-criteria decision making is used to select particular alternatives based on different criteria. The objective of this article is to present some Machine Learning models applied to a data set in order to select the best alternative according to the criteria using the TOPSIS method. The deductive method and the scanning research technique were applied to study a case study on the Wisconsin Breast Cancer dataset, which seeks to evaluate and compare the performance and effectiveness of machine learning models using the TOPSIS.

Idioma original	Inglés
Título de la publicación alojada	Advances in Artificial Intelligence, Software and Systems Engineering - Proceedings of the AHFE 2020 Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing
Editores	Tareq Ahram
Editorial	Springer
Páginas	119-126
Número de páginas	8
ISBN (versión impresa)	9783030513276
DOI	https://doi.org/10.1007/978-3-030-51328-3_18
Estado	Publicada - 2021
Evento	AHFE Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, 2020 - San Diego, Estados Unidos Duración: 16 jul. 2020 → 20 jul. 2020

Serie de la publicación

Nombre	Advances in Intelligent Systems and Computing
Volumen	1213 AISC
ISSN (versión impresa)	2194-5357
ISSN (versión digital)	2194-5365

Conferencia

Conferencia	AHFE Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, 2020
País/Territorio	Estados Unidos
Ciudad	San Diego
Período	16/07/20 → 20/07/20

Nota bibliográfica

Funding Information:
Authors want to thank the Grupo de Investigaci?n en Inteligencia Artificial y Reconocimiento Facial (GIIAR) and the Universidad Polit?cnica Salesiana for supporting this research.

Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

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10.1007/978-3-030-51328-3_18

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Vazquezl, M. Y. L., Briones Peñafiel, L. A., Sanchez Muñoz, S. X., & Martinez, M. A. Q. (2021). A Framework for Selecting Machine Learning Models Using TOPSIS. En T. Ahram (Ed.), Advances in Artificial Intelligence, Software and Systems Engineering - Proceedings of the AHFE 2020 Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing (pp. 119-126). (Advances in Intelligent Systems and Computing; Vol. 1213 AISC). Springer. https://doi.org/10.1007/978-3-030-51328-3_18

Vazquezl, Maikel Yelandi Leyva ; Briones Peñafiel, Luis Andy ; Sanchez Muñoz, Steven Xavier et al. / A Framework for Selecting Machine Learning Models Using TOPSIS. Advances in Artificial Intelligence, Software and Systems Engineering - Proceedings of the AHFE 2020 Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing. editor / Tareq Ahram. Springer, 2021. pp. 119-126 (Advances in Intelligent Systems and Computing).

@inproceedings{39761bb846af411fb341a1398117cf60,

title = "A Framework for Selecting Machine Learning Models Using TOPSIS",

abstract = "In machine learning, it is common when multiple algorithms are applied to different data sets that are complex because of their accelerated growth, a decision problem arises, i.e., how to select the algorithm with the best performance? This has generated the need to implement new information analysis techniques to support decision making. The technique of multi-criteria decision making is used to select particular alternatives based on different criteria. The objective of this article is to present some Machine Learning models applied to a data set in order to select the best alternative according to the criteria using the TOPSIS method. The deductive method and the scanning research technique were applied to study a case study on the Wisconsin Breast Cancer dataset, which seeks to evaluate and compare the performance and effectiveness of machine learning models using the TOPSIS.",

keywords = "Breast cancer, Breast cancer Wisconsin dataset, Data set, Machine learning, TOPSIS",

author = "Vazquezl, {Maikel Yelandi Leyva} and {Briones Pe{\~n}afiel}, {Luis Andy} and {Sanchez Mu{\~n}oz}, {Steven Xavier} and Martinez, {Miguel Angel Quiroz}",

note = "Funding Information: Authors want to thank the Grupo de Investigaci?n en Inteligencia Artificial y Reconocimiento Facial (GIIAR) and the Universidad Polit?cnica Salesiana for supporting this research. Publisher Copyright: {\textcopyright} 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; AHFE Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, 2020 ; Conference date: 16-07-2020 Through 20-07-2020",

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Vazquezl, MYL, Briones Peñafiel, LA, Sanchez Muñoz, SX & Martinez, MAQ 2021, A Framework for Selecting Machine Learning Models Using TOPSIS. En T Ahram (ed.), Advances in Artificial Intelligence, Software and Systems Engineering - Proceedings of the AHFE 2020 Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing. Advances in Intelligent Systems and Computing, vol. 1213 AISC, Springer, pp. 119-126, AHFE Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, 2020, San Diego, Estados Unidos, 16/07/20. https://doi.org/10.1007/978-3-030-51328-3_18

A Framework for Selecting Machine Learning Models Using TOPSIS. / Vazquezl, Maikel Yelandi Leyva; Briones Peñafiel, Luis Andy ; Sanchez Muñoz, Steven Xavier et al.
Advances in Artificial Intelligence, Software and Systems Engineering - Proceedings of the AHFE 2020 Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing. ed. / Tareq Ahram. Springer, 2021. p. 119-126 (Advances in Intelligent Systems and Computing; Vol. 1213 AISC).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

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AU - Vazquezl, Maikel Yelandi Leyva

AU - Briones Peñafiel, Luis Andy

AU - Sanchez Muñoz, Steven Xavier

AU - Martinez, Miguel Angel Quiroz

N1 - Funding Information: Authors want to thank the Grupo de Investigaci?n en Inteligencia Artificial y Reconocimiento Facial (GIIAR) and the Universidad Polit?cnica Salesiana for supporting this research. Publisher Copyright: © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2021

Y1 - 2021

N2 - In machine learning, it is common when multiple algorithms are applied to different data sets that are complex because of their accelerated growth, a decision problem arises, i.e., how to select the algorithm with the best performance? This has generated the need to implement new information analysis techniques to support decision making. The technique of multi-criteria decision making is used to select particular alternatives based on different criteria. The objective of this article is to present some Machine Learning models applied to a data set in order to select the best alternative according to the criteria using the TOPSIS method. The deductive method and the scanning research technique were applied to study a case study on the Wisconsin Breast Cancer dataset, which seeks to evaluate and compare the performance and effectiveness of machine learning models using the TOPSIS.

AB - In machine learning, it is common when multiple algorithms are applied to different data sets that are complex because of their accelerated growth, a decision problem arises, i.e., how to select the algorithm with the best performance? This has generated the need to implement new information analysis techniques to support decision making. The technique of multi-criteria decision making is used to select particular alternatives based on different criteria. The objective of this article is to present some Machine Learning models applied to a data set in order to select the best alternative according to the criteria using the TOPSIS method. The deductive method and the scanning research technique were applied to study a case study on the Wisconsin Breast Cancer dataset, which seeks to evaluate and compare the performance and effectiveness of machine learning models using the TOPSIS.

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Vazquezl MYL, Briones Peñafiel LA, Sanchez Muñoz SX, Martinez MAQ. A Framework for Selecting Machine Learning Models Using TOPSIS. En Ahram T, editor, Advances in Artificial Intelligence, Software and Systems Engineering - Proceedings of the AHFE 2020 Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing. Springer. 2021. p. 119-126. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-51328-3_18

A Framework for Selecting Machine Learning Models Using TOPSIS

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