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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Authors want to thank the Grupo de Investigación en Inteligencia Artificial y Reconocimiento Facial (GIIAR) and the Universidad Politécnica Salesiana for supporting this research.
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Vazquezl, M.Y.L., Peñafiel, L.A.B., Muñoz, S.X.S., Martinez, M.A.Q. (2021). A Framework for Selecting Machine Learning Models Using TOPSIS. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1213. Springer, Cham. https://doi.org/10.1007/978-3-030-51328-3_18
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DOI: https://doi.org/10.1007/978-3-030-51328-3_18
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