G-Forest: An ensemble method for cost-sensitive feature selection in gene expression microarrays.
Mai AbdullaMohammad T. KhasawnehPublished in: Artif. Intell. Medicine (2020)
Keyphrases
- cost sensitive
- gene expression
- ensemble methods
- feature selection
- uncertainty sampling
- gene expression data
- microarray data
- multi class
- microarray
- base classifiers
- feature subset
- naive bayes
- microarray datasets
- decision trees
- misclassification costs
- prediction accuracy
- support vector machine
- ensemble classifier
- gene selection
- benchmark datasets
- classification accuracy
- text categorization
- machine learning methods
- high dimensionality
- dna microarray
- random forest
- active learning
- high throughput
- text classification
- cancer classification
- gene expression profiles
- microarray data analysis
- class distribution
- feature set
- machine learning
- neural network
- classification models
- multi label
- model selection
- ensemble members
- feature space
- training data
- cluster ensemble
- feature extraction