Consensus operators for decision making in Fuzzy Random Forest ensemble

Author

Cadenas, Jose M. ; Garrido, M. Carmen ; Martínez, Alejandro ; Martínez, Raquel

Author_Institution

Dept. Eng. Inf. & Commun., Univ. of Murcia, Murcia, Spain

fYear

2011

fDate

22-24 Nov. 2011

Firstpage

1377

Lastpage

1382

Abstract

When individual classifiers are combined appropriately, we usually obtain a better performance in terms of classification precision. Classifier ensembles are the result of combining several individual classifiers. In this work we propose and compare various consensus based combination methods to obtain the final decision of the ensemble based on fuzzy decision trees in order to improve results. We make a comparative study with several datasets to show the efficiency of the various combination methods.

Keywords

decision trees; fuzzy set theory; mathematical operators; pattern classification; random processes; classification precision; classifier ensemble; consensus based combination method; consensus operator; decision making; fuzzy decision trees; fuzzy random forest ensemble; Decision trees; Fuzzy sets; Intelligent systems; Manganese; Partitioning algorithms; Testing; Vectors; Consensus methods; Decision making; Ensemble; Fuzzy Random Forest; Soft computing;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on

Conference_Location

Cordoba

ISSN

2164-7143

Print_ISBN

978-1-4577-1676-8

Type

conf

DOI

10.1109/ISDA.2011.6121852

Filename

6121852