A semi-supervised rough set and random forest approach for pattern classification of gene expression data.
Pradeep Kumar MallickDebahuti MishraSrikanta PatnaikKailash ShawPublished in: Int. J. Reason. based Intell. Syst. (2016)
Keyphrases
- pattern classification
- rough sets
- random forest
- gene expression data
- semi supervised
- cancer classification
- pattern recognition
- microarray
- gene expression
- feature set
- semi supervised learning
- data sets
- supervised learning
- labeled data
- microarray data
- decision trees
- feature selection
- unlabeled data
- high dimensionality
- data mining
- fold cross validation
- active learning
- ensemble methods
- feature extraction
- gene selection
- pairwise
- data analysis
- unsupervised learning
- fuzzy logic
- multi label
- high throughput
- selected features
- high dimensional data
- high dimensional
- gene expression profiles
- benchmark datasets
- text categorization
- dimensionality reduction
- computer vision
- artificial neural networks