Combination methods in a Fuzzy Random Forest
Piero P. Bonissone, José Manuel Cadenas Figueredo, M. Carmen Garrido, R. Andrés Díaz-Valladares · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · 2008
When individual classifiers are combined appropriately, we usually obtain a better performance in terms of classification precision. Multi-classifiers are the result of combining several individual classifiers. In this work we propose and compare various combination methods to obtain the final decision of the multi-classifier based on a ldquoforestrdquo of randomly generated fuzzy decision trees, i.e., a Fuzzy Random Forest. We propose various forms of weighting decisions on the basis of information obtained from the FRF. We make a comparative study with several databases to show the efficiency of the various combination methods.