Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Leonardo Canete-Sifuentes"'
Publikováno v:
IEEE Access, Vol 11, Pp 139147-139156 (2023)
When facing a classification problem, data science practitioners must search through an armory of methods. Often, practitioners are tempted to use off-the-shelf classifiers, including automated Machine Learning (AutoML) toolboxes; however, stand-alon
Externí odkaz:
https://doaj.org/article/8bb1af672d3949e1883a7cafc099c89d
Publikováno v:
IEEE Access, Vol 9, Pp 110451-110479 (2021)
Decision trees are popular as stand-alone classifiers or as base learners in ensemble classifiers. Mostly, this is due to decision trees having the advantage of being easy to explain. To improve the classification performance of decision trees, some
Externí odkaz:
https://doaj.org/article/dcdce70d229e42d8b3dacf1640174ff0
Autor:
Leonardo Canete-Sifuentes, Raul Monroy, Miguel Angel Medina-Perez, Octavio Loyola-Gonzalez, Francisco Vera Voronisky
Publikováno v:
IEEE Access, Vol 7, Pp 55744-55762 (2019)
There is a growing interest in the development of classifiers based on contrast patterns (CPs); partly due to the advantage of them being able to explain classification results in a language that is easy to understand for an expert. CP-based classifi
Externí odkaz:
https://doaj.org/article/9ddaa1a0f3c84b14be86ef7b1d016261
Autor:
Leonardo Canete-Sifuentes, Diana Laura Aguilar, Kim-Kwang Raymond Choo, Miguel Angel Medina-Pérez, Octavio Loyola-González
Publikováno v:
Future Generation Computer Systems. 125:71-90
In addition to accuracy, another key desirable characteristic of a classifier is interpretability. While there have been attempts to design contrast pattern-based models that support competitive and understandable classifiers, the utility of contrast
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1a419b6bbdff09f187523ac159177030
https://doi.org/10.1016/j.future.2021.06.046
https://doi.org/10.1016/j.future.2021.06.046
Publikováno v:
IEEE Access, Vol 9, Pp 110451-110479 (2021)
Decision trees are popular as stand-alone classifiers or as base learners in ensemble classifiers. Mostly, this is due to decision trees having the advantage of being easy to explain. To improve the classification performance of decision trees, some
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b74807b73fded4de542c208f72cc00f
https://ieeexplore.ieee.org/document/9505668/
https://ieeexplore.ieee.org/document/9505668/
Autor:
Francisco Vera Voronisky, Miguel Angel Medina-Pérez, Leonardo Canete-Sifuentes, Raúl Monroy, Octavio Loyola-González
Publikováno v:
IEEE Access, Vol 7, Pp 55744-55762 (2019)
There is a growing interest in the development of classifiers based on contrast patterns (CPs); partly due to the advantage of them being able to explain classification results in a language that is easy to understand for an expert. CP-based classifi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fc202877b8a5d849eef45527f1fa2a31
https://doi.org/10.1109/access.2019.2913649
https://doi.org/10.1109/access.2019.2913649
Publikováno v:
Applied Sciences, Vol 14, Iss 4, p 1403 (2024)
Using automated data analysis to understand what makes a play successful in football can enable teams to make data-driven decisions that may enhance their performance throughout the season. Analyzing different types of plays (e.g., corner, penalty, f
Externí odkaz:
https://doaj.org/article/7be0c2c572a64095946d264287c69704