Full Text

Turn on search term navigation

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The presence of machine learning, data mining and related disciplines is increasingly evident in everyday environments. The support for the applications of learning techniques in topics related to economic risk assessment, among other financial topics of interest, is relevant for us as human beings. The content of this paper consists of a proposal of a new supervised learning algorithm and its application in real world datasets related to finance, called D1-NN (Dynamic 1-Nearest Neighbor). The D1-NN performance is competitive against the main state of the art algorithms in solving finance-related problems. The effectiveness of the new D1-NN classifier was compared against five supervised classifiers of the most important approaches (Bayes, nearest neighbors, support vector machines, classifier ensembles, and neural networks), with superior results overall.

Details

Title
Dynamic Nearest Neighbor: An Improved Machine Learning Classifier and Its Application in Finances
Author
Camacho-Urriolagoitia, Oscar 1 ; López-Yáñez, Itzamá 1   VIAFID ORCID Logo  ; Villuendas-Rey, Yenny 1   VIAFID ORCID Logo  ; Camacho-Nieto, Oscar 1   VIAFID ORCID Logo  ; Yáñez-Márquez, Cornelio 2   VIAFID ORCID Logo 

 Centro de Innovación y Desarrollo Tecnológico en Cómputo del Instituto Politécnico Nacional, Juan de Dios Bátiz s/n, GAM, Mexico City 07700, Mexico; [email protected] 
 Centro de Investigación en Computación del Instituto Politécnico Nacional, Juan de Dios Bátiz s/n, GAM, Mexico City 07700, Mexico 
First page
8884
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
20763417
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2580963882
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.