A Memory-Efficient Encoding Method for Processing Mixed-Type Data on Machine Learning

The most common machine-learning methods solve supervised and unsupervised problems based on datasets where the problem’s features belong to a numerical space. However, many problems often include data where numerical and categorical data coexist, which represents a challenge to manage them. To tran...

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Detalles Bibliográficos
Autores principales: Lopez-Arevalo, Ivan, Aldana-Bobadilla, Edwin, Molina-Villegas, Alejandro, Galeana-Zapién, Hiram, Muñiz-Sanchez, Victor, Gausin-Valle, Saul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7763608/
https://www.ncbi.nlm.nih.gov/pubmed/33316972
http://dx.doi.org/10.3390/e22121391

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7763608/
https://www.ncbi.nlm.nih.gov/pubmed/33316972
http://dx.doi.org/10.3390/e22121391

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