Leveraging multimodal and feature selection approaches to improve sleep apnea classification performance.
Gokhan MemisMustafa SertAdnan YaziciPublished in: SIU (2017)
Keyphrases
- feature selection
- classification accuracy
- classification models
- machine learning
- feature extraction
- sleep apnea
- feature selection algorithms
- text classification
- high dimensionality
- sleep stage
- support vector
- decision trees
- pattern recognition
- feature space
- machine learning algorithms
- feature subset
- support vector machine
- model selection
- supervised and unsupervised learning
- support vector machine svm
- mutual information
- machine learning methods
- feature set
- brain image analysis
- classification performances
- class separability
- bayesian classifier
- text categorization
- unsupervised learning
- semi supervised
- supervised learning
- classification rate
- selected features
- discriminative features
- dimension reduction
- feature ranking
- accurate classification
- class labels
- feature selection and classification
- training set
- image processing
- multi modal