Fast and Scalable Feature Selection for Gene Expression Data Using Hilbert-Schmidt Independence Criterion.
Mehrdad J. GangehHadi ZarkoobAli GhodsiPublished in: IEEE ACM Trans. Comput. Biol. Bioinform. (2017)
Keyphrases
- gene expression data
- feature selection
- hilbert schmidt independence criterion
- high dimensionality
- microarray data
- text categorization
- feature space
- gene expression data sets
- gene expression
- microarray
- gene regulatory networks
- gene expression datasets
- classification accuracy
- gene selection
- gene expression data analysis
- gene expression analysis
- cancer classification
- text classification
- microarray datasets
- gene expression profiles
- support vector
- dimensionality reduction
- machine learning
- feature ranking
- gene expression profiling
- analysis of gene expression data
- dna microarray
- data sets
- knn
- high dimensional
- support vector machine
- feature extraction
- regulatory networks
- feature set
- multiple kernel learning
- gene expression patterns
- tumor classification
- face recognition