Feature selection using neighborhood entropy-based uncertainty measures for gene expression data classification.
Lin SunXiaoyu ZhangYu-Hua QianJiucheng XuShiguang ZhangPublished in: Inf. Sci. (2019)
Keyphrases
- gene expression data
- feature selection
- high dimensionality
- cancer classification
- microarray datasets
- microarray data
- microarray
- gene expression data sets
- gene expression
- dna microarray
- tumor classification
- gene expression analysis
- gene expression profiles
- dna microarray data
- gene expression datasets
- classification accuracy
- cancer diagnosis
- text classification
- support vector
- gene selection
- colon cancer
- feature space
- analysis of gene expression data
- gene regulatory networks
- gene expression microarray data
- microarray gene expression data
- gene expression data analysis
- machine learning
- classification models
- feature set
- feature selection algorithms
- high dimensional data
- text categorization
- microarray data analysis
- high dimensional
- support vector machine
- feature subset
- decision trees
- small sample size
- gene networks
- cancer datasets
- data sets
- gene expression profiling
- feature extraction
- support vector machine svm
- regulatory networks
- machine learning methods
- fold cross validation
- low dimensional
- unsupervised learning
- data analysis
- pattern recognition
- high throughput