SDRDPy: An application to graphically visualize the knowledge obtained with supervised descriptive rule algorithms

Fuente: arXiv
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Autores principales: Padilla-Rascon, M. A., Gonzalez, P., Carmona, C. J.
Formato: Preprint
Publicado: 2024
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author Padilla-Rascon, M. A.
Gonzalez, P.
Carmona, C. J.
author_facet Padilla-Rascon, M. A.
Gonzalez, P.
Carmona, C. J.
contents SDRDPy is a desktop application that allows experts an intuitive graphic and tabular representation of the knowledge extracted by any supervised descriptive rule discovery algorithm. The application is able to provide an analysis of the data showing the relevant information of the data set and the relationship between the rules, data and the quality measures associated for each rule regardless of the tool where algorithm has been executed. All of the information is presented in a user-friendly application in order to facilitate expert analysis and also the exportation of reports in different formats.
format Preprint
id arxiv_https___arxiv_org_abs_2401_17783
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle SDRDPy: An application to graphically visualize the knowledge obtained with supervised descriptive rule algorithms
Padilla-Rascon, M. A.
Gonzalez, P.
Carmona, C. J.
Artificial Intelligence
SDRDPy is a desktop application that allows experts an intuitive graphic and tabular representation of the knowledge extracted by any supervised descriptive rule discovery algorithm. The application is able to provide an analysis of the data showing the relevant information of the data set and the relationship between the rules, data and the quality measures associated for each rule regardless of the tool where algorithm has been executed. All of the information is presented in a user-friendly application in order to facilitate expert analysis and also the exportation of reports in different formats.
title SDRDPy: An application to graphically visualize the knowledge obtained with supervised descriptive rule algorithms
topic Artificial Intelligence
url https://arxiv.org/abs/2401.17783