Gabrys, B., 2002. Combining Neuro-Fuzzy Classifiers for Improved Generalisation and Reliability. In: Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on, May 12-17, 2002, Hilton Hawaiian Village Hotel, Honolulu, Hawaii, pp. 2410-2415.
Full text available as:
Gabrys_WCCI2002.pdf - Published Version
In this paper a combination of neuro-fuzzy classifiers for improved classification performance and reliability is considered. A general fuzzy min-max (GFMM) classifier with agglomerative learning algorithm is used as a main building block. An alternative approach to combining individual classifier decisions involving the combination at the classifier model level is proposed. The resulting classifier complexity and transparency is comparable with classifiers generated during a single crossvalidation procedure while the improved classification performance and reduced variance is comparable to the ensemble of classifiers with combined (averaged/voted) decisions. We also illustrate how combining at the model level can be used for speeding up the training of GFMM classifiers for large data sets.
|Item Type:||Conference or Workshop Item (Paper)|
|Subjects:||Generalities > Computer Science and Informatics > Artificial Intelligence|
Generalities > Computer Science and Informatics
|Group:||Faculty of Science & Technology|
|Deposited By:||INVALID USER|
|Deposited On:||19 Dec 2008 19:18|
|Last Modified:||10 Sep 2014 14:43|
Downloads per month over past year
|Repository Staff Only -|