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Cross-Domain Classification Model With Knowledge Utilization Maximization for Recognition of Epileptic EEG Signals
- Source :
- IEEE/ACM transactions on computational biology and bioinformatics. 18(1)
- Publication Year :
- 2020
-
Abstract
- Conventional classification models for epileptic EEG signal recognition need sufficient labeled samples as training dataset. In addition, when training and testing EEG signal samples are collected from different distributions, for example, due to differences in patient groups or acquisition devices, such methods generally cannot perform well. In this paper, a cross-domain classification model with knowledge utilization maximization called CDC-KUM is presented, which takes advantage of the data global structure provided by the labeled samples in the related domain and unlabeled samples in the current domain. Through mapping the data into kernel space, the pairwise constraint regularization term is combined together the predictive differences of the labeled data in the source domain. Meanwhile, the soft clustering regularization term using quadratic weights and Gini-Simpson diversity is applied to exploit the distribution information of unlabeled data in the target domain. Experimental results show that CDC-KUM model outperformed several traditional non-transfer and transfer classification methods for recognition of epileptic EEG signals.
- Subjects :
- Fuzzy clustering
Computer science
0206 medical engineering
02 engineering and technology
Electroencephalography
Regularization (mathematics)
Data modeling
Machine Learning
Quadratic equation
Genetics
medicine
Humans
Epilepsy
medicine.diagnostic_test
business.industry
Applied Mathematics
Pattern recognition
Signal Processing, Computer-Assisted
Maximization
ComputingMethodologies_PATTERNRECOGNITION
Pairwise comparison
Artificial intelligence
business
Transfer of learning
020602 bioinformatics
Algorithms
Biotechnology
Subjects
Details
- ISSN :
- 15579964
- Volume :
- 18
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- IEEE/ACM transactions on computational biology and bioinformatics
- Accession number :
- edsair.doi.dedup.....7948d81c65bf5a449178bdc7504ef5be