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

Artificial Intelligence and Facial Recognition Research Group (GIIAR)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

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.

Original language	English
Title of host publication	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
Editors	Tareq Ahram
Publisher	Springer
Pages	119-126
Number of pages	8
ISBN (Print)	9783030513276
DOIs	https://doi.org/10.1007/978-3-030-51328-3_18
State	Published - 2021
Event	AHFE Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, 2020 - San Diego, United States Duration: 16 Jul 2020 → 20 Jul 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1213 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	AHFE Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, 2020
Country/Territory	United States
City	San Diego
Period	16/07/20 → 20/07/20

Bibliographical 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:
© 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.

Keywords

Breast cancer
Breast cancer Wisconsin dataset
Data set
Machine learning
TOPSIS

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

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

Cite this

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. In 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).

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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.",

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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.; null ; 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. in 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, United States, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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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KW - Data set

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

M3 - Conference contribution

AN - SCOPUS:85088508933

SN - 9783030513276

T3 - Advances in Intelligent Systems and Computing

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BT - 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

A2 - Ahram, Tareq

PB - Springer

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ER -

Vazquezl MYL, Briones Peñafiel LA, Sanchez Muñoz SX, Martinez MAQ. A Framework for Selecting Machine Learning Models Using TOPSIS. In 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). https://doi.org/10.1007/978-3-030-51328-3_18

A Framework for Selecting Machine Learning Models Using TOPSIS

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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