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Belief Entropy Tree and Random Forest: Learning from Data with Continuous Attributes and Evidential Labels
As well-known machine learning methods, decision trees are widely applied in classification and recognition areas. In this paper, with the uncertainty of labels handled by belief functions, a new decision tree method based on belief entropy is proposed and then extended to random forest. With the Ga...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9141821/ https://www.ncbi.nlm.nih.gov/pubmed/35626490 http://dx.doi.org/10.3390/e24050605 |