On Applying Random Oracles to Fuzzy Rule-Based Classifier Ensembles for High Complexity Datasets
Krzysztof Trawinski, Oscar Cordon, Arnaud Quirin
Available Online August 2013.
- https://doi.org/10.2991/eusflat.2013.92How to use a DOI?
- Fuzzy rule-based classifier ensembles random oracles bagging classifier fusion classifier selection high complexity datasets
- Fuzzy rule-based systems suffer from the so-called curse of dimensionality when applied to high com- plexity datasets, which consist of a large number of variables and/or examples. Fuzzy rule-based clas- sifier ensembles have shown to be a good approach to deal with this kind of problems. In this contri- bution, we would like to take one step forward and extend this approach with two variants of random oracles with the aim that this classical method in- duces more diversity and in this way improves the performance of the system. We will conduct ex- haustive experiments considering 29 UCI and KEEL datasets with high complexity (considering both a number of attributes as well as a number of exam- ples). The results obtained are promising and show that random oracles fuzzy rule-based ensembles can be competitive with random oracles ensembles using state-of-the-art base classifiers in terms of accuracy, when dealing with high complexity datasets.
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Cite this article
TY - CONF AU - Krzysztof Trawinski AU - Oscar Cordon AU - Arnaud Quirin PY - 2013/08 DA - 2013/08 TI - On Applying Random Oracles to Fuzzy Rule-Based Classifier Ensembles for High Complexity Datasets BT - 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13) PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/eusflat.2013.92 DO - https://doi.org/10.2991/eusflat.2013.92 ID - Trawinski2013/08 ER -