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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2019
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| Materias: | |
| 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 |