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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 languageEnglish
Title of host publicationAdvances 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
EditorsTareq Ahram
PublisherSpringer
Pages119-126
Number of pages8
ISBN (Print)9783030513276
DOIs
StatePublished - 2021
EventAHFE Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, 2020 - San Diego, United States
Duration: 16 Jul 202020 Jul 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1213 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceAHFE Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, 2020
Country/TerritoryUnited States
CitySan Diego
Period16/07/2020/07/20

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

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