A Rule Based Approach to Classification of EEG Datasets: A Comparison Between ANFIS and Rough Sets

被引：0

作者：

Jahankhani, Pari ^{[1
]}

Revett, Kenneth ^{[1
]}

Kodogiannis, Vassilis ^{[1
]}

机构：

[1] Univ Westminster, Sch Comp Sci, London HA1 3TP, England

来源：

NEUREL 2008: NINTH SYMPOSIUM ON NEURAL NETWORK APPLICATIONS IN ELECTRICAL ENGINEERING, PROCEEDINGS | 2008年

关键词：

electroencephalography; Neuro-fuzzy systems; PCA; Rough sets; wavelets;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：148 / 151

页数：4

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