Genetic Clustering Algorithm-Based Feature Selection and Divergent Random Forest for Multiclass Cancer Classification Using Gene Expression Data.
L. SenbagamalarS. LogeswariPublished in: Int. J. Comput. Intell. Syst. (2024)
Keyphrases
- multi class
- cancer classification
- random forest
- feature selection
- gene expression data
- clustering algorithm
- base classifiers
- feature set
- gene expression profiling
- cancer diagnosis
- microarray data
- gene selection
- random forests
- support vector machine
- decision trees
- microarray
- k means
- fold cross validation
- dna microarray
- ensemble methods
- cost sensitive
- high dimensionality
- data clustering
- gene expression profiles
- gene expression
- text categorization
- feature selection algorithms
- support vector
- pairwise
- cluster analysis
- ensemble learning
- clustering method
- clustering analysis
- feature ranking
- dimensionality reduction
- feature subset
- microarray datasets
- text classification
- classification accuracy
- active learning
- feature extraction