A Neighborhood Rough Sets-Based Attribute Reduction Method Using Lebesgue and Entropy Measures

For continuous numerical data sets, neighborhood rough sets-based attribute reduction is an important step for improving classification performance. However, most of the traditional reduction algorithms can only handle finite sets, and yield low accuracy and high cardinality. In this paper, a novel...

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Detalles Bibliográficos
Autores principales: Sun, Lin, Wang, Lanying, Xu, Jiucheng, Zhang, Shiguang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514624/
https://www.ncbi.nlm.nih.gov/pubmed/33266854
http://dx.doi.org/10.3390/e21020138

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514624/
https://www.ncbi.nlm.nih.gov/pubmed/33266854
http://dx.doi.org/10.3390/e21020138

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