Random forests have proved to be very effective classifiers, which can achieve very high accuracies. Although a number of papers have discussed the use of fuzzy sets for coping with uncertain data in decision tree learning, fuzzy random forests have not been particularly investigated in the fuzzy community. In this paper, we first propose a simple method for generating fuzzy decision trees by creating fuzzy partitions for continuous variables during the learning phase. Then, we discuss how the method can be used for generating forests of fuzzy decision trees. Finally, we show how these fuzzy random forests achieve accuracies higher than two fuzzy rule-based classifiers recently proposed in the literature. Also, we highlight how fuzzy random forests are more tolerant to noise in datasets than classical crisp random forests.

A new approach to fuzzy random forest generation

MARCELLONI, FRANCESCO;SEGATORI, ARMANDO
2015-01-01

Abstract

Random forests have proved to be very effective classifiers, which can achieve very high accuracies. Although a number of papers have discussed the use of fuzzy sets for coping with uncertain data in decision tree learning, fuzzy random forests have not been particularly investigated in the fuzzy community. In this paper, we first propose a simple method for generating fuzzy decision trees by creating fuzzy partitions for continuous variables during the learning phase. Then, we discuss how the method can be used for generating forests of fuzzy decision trees. Finally, we show how these fuzzy random forests achieve accuracies higher than two fuzzy rule-based classifiers recently proposed in the literature. Also, we highlight how fuzzy random forests are more tolerant to noise in datasets than classical crisp random forests.
978-1-4673-7428-6
File in questo prodotto:
File Dimensione Formato  
Marcelloni_760305.pdf

accesso aperto

Tipologia: Documento in Pre-print
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 287.15 kB
Formato Adobe PDF
287.15 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/760305
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 12
  • ???jsp.display-item.citation.isi??? 0
social impact