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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

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 originalInglés
Título de la publicación alojadaAdvances 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
EditoresTareq Ahram
EditorialSpringer
Páginas119-126
Número de páginas8
ISBN (versión impresa)9783030513276
DOI
EstadoPublicada - 2021
EventoAHFE Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, 2020 - San Diego, Estados Unidos
Duración: 16 jul 202020 jul 2020

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
Volumen1213 AISC
ISSN (versión impresa)2194-5357
ISSN (versión digital)2194-5365

Conferencia

ConferenciaAHFE Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, 2020
País/TerritorioEstados Unidos
CiudadSan Diego
Período16/07/2020/07/20

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.

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