|Chapter title||A rule based approach to classification of EEG datasets: a comparison between ANFIS and rough sets|
|Authors||Jahankhani, P., Revett, K. and Kodogiannis, V.|
|Editors||Reljin, B. and Stankovic, S.|
This paper compares two different rule based classification methods in order to evaluate their relative efficiacy with respect to classification accuracy and the caliber of the resulting rules. Specifically, the application of Adaptive Neuro-Fuzzy Inference System (ANFIS) and rough sets were deployed on a complete dataset consisting of electroencephalogram (EEG) data. The results indicate that both were able to classify this dataset accurately and the number of rules were similar in both cases, provided the dataset was pre-processed using PCA in the case of ANFIS.
|Keywords||Neuro-fuzzy systems, PCA, Rough sets, electroencephalography, wavelets|
|Book title||Proceedings of the Ninth Symposium on Neural Network Applications in Electrical Engineering: NEUREL 2008, September 27-28, 2008, Faculty of Electrical Engineering, University of Belgrade|
|Place of publication||Los Alamitos, USA|
|Digital Object Identifier (DOI)||https://doi.org/10.1109/NEUREL.2008.4685599|