On Applying Random Oracles to Fuzzy Rule-Based Classifier Ensembles for High Complexity Datasets

DOI

10.2991/eusflat.2013.92How to use a DOI?

Keywords

Fuzzy rule-based classifier ensembles random oracles bagging classifier fusion classifier selection high complexity datasets

Abstract

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.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13)
PB  - Atlantis Press
SP  - 645
EP  - 652
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2013.92
DO  - 10.2991/eusflat.2013.92
ID  - Trawinski2013/08
ER  -