Survival Risk Prediction of Esophageal Cancer Based on Self-Organizing Maps Clustering and Support Vector Machine Ensembles.
Junwei SunYuli YangYanfeng WangLidong WangXin SongXueke ZhaoPublished in: IEEE Access (2020)
Keyphrases
- support vector machine
- stock market prediction
- kaplan meier
- clustering algorithm
- prediction accuracy
- clustering method
- outcome prediction
- k means
- neural network ensemble
- predictive clustering trees
- svm classifier
- decision trees
- ensemble classifier
- survival analysis
- hierarchical clustering
- ensemble methods
- multi class
- training data
- survival prediction
- prediction error
- support vector regression
- neural network
- data clustering
- feature selection
- support vector
- feature vectors
- breast cancer
- support vector machine svm
- self organizing maps
- data sets
- machine learning
- gene expression profiles
- early detection
- decision boundary
- risk assessment
- kernel methods
- high dimensionality
- random forests
- imbalanced data
- ensemble learning
- knn
- cluster analysis
- spectral clustering
- radial basis function