A comparison of PSO and GA approaches for gene selection and classification of microarray data.
José García-NietoEnrique AlbaLaetitia JourdanEl-Ghazali TalbiPublished in: GECCO (2007)
Keyphrases
- microarray data
- gene selection
- cancer classification
- relevant genes
- microarray data analysis
- dna microarray data
- microarray
- gene expression profiles
- microarray datasets
- dna microarray
- feature selection
- colon cancer
- ovarian cancer
- gene expression data
- cancer diagnosis
- small sample size
- informative genes
- gene expression
- genetic algorithm ga
- small number of samples
- support vector machine svm
- microarray classification
- microarray analysis
- high dimensional
- data sets
- gene expression datasets
- machine learning methods
- pattern recognition
- support vector machine
- random forest
- biologically relevant
- cluster analysis
- machine learning algorithms
- gene expression analysis
- support vector
- differentially expressed genes
- feature ranking
- gene networks
- microarray technology
- gene clusters
- high dimensionality
- high throughput
- selected genes
- k nearest neighbor
- molecular biology
- gene expression data sets
- early detection
- supervised learning
- feature vectors
- feature space
- decision trees
- machine learning
- data mining