One of the current challenges in the field of data mining is to develop techniques to analyze uncertain data. Among these techniques, in this paper we focus on decision tree classifiers. In particular, we introduce a new data structure that can be used to represent multiple decision trees generated from uncertain datasets.
Titolo: | Uncertainty in decision tree classifiers |
Autore/i: | MAGNANI, MATTEO; MONTESI, DANILO |
Autore/i Unibo: | |
Anno: | 2010 |
Titolo del libro: | International conference on Scalable Uncertainty Management |
Pagina iniziale: | 250 |
Pagina finale: | 263 |
Abstract: | One of the current challenges in the field of data mining is to develop techniques to analyze uncertain data. Among these techniques, in this paper we focus on decision tree classifiers. In particular, we introduce a new data structure that can be used to represent multiple decision trees generated from uncertain datasets. |
Data prodotto definitivo in UGOV: | 20-feb-2011 |
Appare nelle tipologie: | 4.01 Contributo in Atti di convegno |
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