Gene expression data analyses for supervised prostate cancer classification based on feature subset selection combined with different classifiers.
Sara Haddou BouazzaAbdelouhab ZeroualKhalid AuhmaniPublished in: ICMCS (2016)
Keyphrases
- feature subset selection
- feature selection
- cancer classification
- gene expression data
- feature subset
- gene expression profiling
- cancer datasets
- gene selection
- microarray data
- feature set
- microarray
- cancer diagnosis
- gene expression datasets
- high dimensionality
- microarray gene expression data
- support vector
- feature selection algorithms
- gene expression profiles
- text classification
- naive bayes
- random forest
- feature ranking
- classification models
- selected features
- colon cancer
- text categorization
- dimensionality reduction
- support vector machine
- gene regulatory networks
- machine learning
- classification accuracy
- data sets
- gene expression
- dna microarray
- model selection
- informative genes
- feature space
- selection algorithm
- multi class
- feature extraction
- unsupervised learning
- gene expression analysis
- supervised learning
- training data
- knn
- fold cross validation
- microarray datasets
- ensemble methods
- class labels
- data mining
- high dimensional
- pattern recognition