A novel automatic diagnostic approach based on nystagmus feature selection and neural network classification.
Amine Ben SlamaAymen MouelhiHanene SahliSondes ManoubiMamia Ben SalahMounir SayadiHedi TrabelsiFarhat FnaiechPublished in: IECON (2016)
Keyphrases
- feature selection
- neural network
- classification accuracy
- classification models
- feature set
- text classification
- support vector
- pattern recognition
- feature extraction
- feature space
- support vector machine
- machine learning
- learning vector quantization
- pattern classification
- image classification
- feature subset
- discriminative features
- decision trees
- feature vectors
- method for feature selection
- high dimensionality
- small sample
- multi layer perceptron
- genetic algorithm
- statistical classification
- unsupervised learning
- mutual information
- classification rate
- information gain
- classification performances
- feature ranking
- fold cross validation
- feature selection algorithms
- cross validation
- feature selection and classification
- training data
- back propagation
- support vector machine svm
- bayes classifier
- automatic relevance determination
- support vector classification
- artificial neural networks
- knn
- multi class
- data pre processing
- dimensionality reduction
- microarray datasets
- microarray data
- model selection
- text categorization
- instance selection
- svm classifier
- classification method
- fuzzy rules