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A novel decision tree classification based on post-pruning with Bayes minimum risk
Pruning is applied in order to combat over-fitting problem where the tree is pruned back with the goal of identifying decision tree with the lowest error rate on previously unobserved instances, breaking ties in favour of smaller trees with high accuracy. In this paper, pruning with Bayes minimum ri...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2018
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5884499/ https://www.ncbi.nlm.nih.gov/pubmed/29617369 http://dx.doi.org/10.1371/journal.pone.0194168 |