UM
Adaptive Hybrid Feature Selection-Based Classifier Ensemble for Epileptic Seizure Classification
Alzami, Farrikh; Tang, Juan; Yu, Zhiwen; Wu, Si; Chen, C. L. Philip; You, Jane; Zhang, Jun
2018
Source PublicationIEEE ACCESS
ISSN2169-3536
Volume6Pages:29132-29145
AbstractFeature selection and ensemble learning can be used to improve the accuracy and robustness of epileptic seizure detection and classification. Unfortunately, a few studies have fully utilized feature selection and ensemble learning. In this paper, we present an adaptive hybrid feature selection-based classifier ensemble (AHFSE) for epileptic seizure classification. The AHFSE creates new sample subsets in every bootstrap using adaptive hybrid feature selection. It combines them using rank aggregation to obtain a distinguished subset of features. These new samples subsets are then fed into a classifier. Finally, majority voting is used to complete the detection and classification tasks. The AHFSE is designed to obtain an optimized subset of features based on the different samples in every bootstrap, which have a tendency to generate different results with respect to rank aggregation. With discrete wavelet transform, the experiments based on binary and multi-class tasks show that the AHFSE performs well on the Bonn data set and improves the specificity, sensitivity, or accuracy of the selected features by combining the subsets of different feature selections to obtain new samples within the bagging process. Furthermore, the adaptive process helps the framework obtain the optimum combination of the feature selection algorithm. The AHFSE also obtains more desirable final results in several perspectives, such as: 1) compared with other feature selection methods; 2) compared with other ensemble methods; and 3) compared with other research that uses discrete wavelet transform as a preprocessing step.
KeywordEpileptic seizure detection and classification discrete wavelet transform hybrid feature selection classifier ensemble bagging rank aggregation adaptive genetic algorithm optimization machine learning
DOI10.1109/ACCESS.2018.2838559
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000435521100013
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
The Source to ArticleWOS
Fulltext Access
Citation statistics
Cited Times [WOS]:3   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Recommended Citation
GB/T 7714
Alzami, Farrikh,Tang, Juan,Yu, Zhiwen,et al. Adaptive Hybrid Feature Selection-Based Classifier Ensemble for Epileptic Seizure Classification[J]. IEEE ACCESS,2018,6:29132-29145.
APA Alzami, Farrikh.,Tang, Juan.,Yu, Zhiwen.,Wu, Si.,Chen, C. L. Philip.,...&Zhang, Jun.(2018).Adaptive Hybrid Feature Selection-Based Classifier Ensemble for Epileptic Seizure Classification.IEEE ACCESS,6,29132-29145.
MLA Alzami, Farrikh,et al."Adaptive Hybrid Feature Selection-Based Classifier Ensemble for Epileptic Seizure Classification".IEEE ACCESS 6(2018):29132-29145.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Alzami, Farrikh]'s Articles
[Tang, Juan]'s Articles
[Yu, Zhiwen]'s Articles
Baidu academic
Similar articles in Baidu academic
[Alzami, Farrikh]'s Articles
[Tang, Juan]'s Articles
[Yu, Zhiwen]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Alzami, Farrikh]'s Articles
[Tang, Juan]'s Articles
[Yu, Zhiwen]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.