LPDA: A new classification method based on linear programming
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http://hdl.handle.net/10045/124997
Título: | LPDA: A new classification method based on linear programming |
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Autor/es: | Nueda, María José | Gandía, Carmen | Molina Vila, Mariola D. |
Grupo/s de investigación o GITE: | Grupo de Estadística Aplicada (GESTA) | Bioquímica Aplicada/Applied Biochemistry (AppBiochem) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Matemáticas |
Palabras clave: | LPDA | Classification method | Linear programming |
Área/s de conocimiento: | Estadística e Investigación Operativa |
Fecha de publicación: | 7-jul-2022 |
Editor: | Public Library of Science (PLoS) |
Cita bibliográfica: | Nueda MJ, Gandía C, Molina MD (2022) LPDA: A new classification method based on linear programming. PLoS ONE 17(7): e0270403. https://doi.org/10.1371/journal.pone.0270403 |
Resumen: | The search of separation hyperplanes is an efficient way to find rules with classification purposes. This paper presents an alternative mathematical programming formulation to existing methods to find a discriminant hyperplane. The hyperplane H is found by minimizing the sum of all the distances to the area assigned to the group each individual belongs to. It results in a convex optimization problem for which we find an equivalent linear programming problem. We demonstrate that H exists when the centroids of the two groups are not equal. The method is effective dealing with low and high dimensional data where reduction of the dimension is proposed to avoid overfitting problems. We show the performance of this approach with different data sets and comparisons with other classifications methods. The method is called LPDA and it is implemented in a R package available in https://github.com/mjnueda/lpda. |
Patrocinador/es: | This research has been partially supported by Generalitat Valenciana, Grant GV/2017/177. |
URI: | http://hdl.handle.net/10045/124997 |
ISSN: | 1932-6203 |
DOI: | 10.1371/journal.pone.0270403 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2022 Nueda et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1371/journal.pone.0270403 |
Aparece en las colecciones: | INV - AppBiochem - Artículos de Revistas INV - GESTA - Artículos de Revistas |
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