LDA-GA-SVM: improved hepatocellular carcinoma prediction through dimensionality reduction and genetically optimized support vector machine.
Liaqat AliIram WajahatNoorbakhsh Amiri GolilarzFazel KeshtkarSyed Ahmad Chan BukhariPublished in: Neural Comput. Appl. (2021)
Keyphrases
- support vector machine
- dimensionality reduction
- linear discriminant analysis
- support vector machine svm
- feature space
- feature selection
- support vector
- svm classifier
- polynomial neural networks
- genetic algorithm ga
- feature extraction
- multi class
- feature vectors
- hyperplane
- high dimensional
- genetic algorithm
- pattern recognition
- high dimensional data
- machine learning
- data points
- principal component analysis
- genetically optimized
- kernel function
- kernel methods
- low dimensional
- high dimensionality
- decision boundary
- support vectors
- training data
- face recognition
- kernel pca
- fitness function
- design procedure
- training set
- neural network
- prediction model
- principal components
- radial basis function
- generative model
- subspace methods
- knn
- design methodology
- ls svm
- fuzzy logic
- data mining
- kernel density estimator
- learning algorithm
- topic models