K-Means Clustering with Infinite Feature Selection for Classification Tasks in Gene Expression Data.
Muhammad Akmal bin RemliKauthar Mohd DaudHui Wen NiesMohd Saberi MohamadSafaai DerisSigeru OmatuShahreen KasimGhazali SulongPublished in: PACBB (2017)
Keyphrases
- gene expression data
- feature selection
- microarray data
- gene expression
- high dimensionality
- microarray
- gene expression data sets
- gene regulatory networks
- gene selection
- gene expression analysis
- data sets
- text categorization
- gene expression data analysis
- cancer classification
- microarray datasets
- feature set
- gene expression datasets
- gene networks
- machine learning
- gene expression profiling
- feature selection algorithms
- microarray gene expression data
- classification accuracy
- neural network
- analysis of gene expression data
- dna microarray
- cancer diagnosis
- gene expression profiles
- feature extraction
- regulatory networks
- high throughput
- k means
- high dimensional data
- cluster analysis
- feature space
- support vector
- gene expression microarray data
- clustering gene expression data
- database
- support vector machine
- biological information
- gene expression patterns
- decision trees
- feature subset
- data clustering
- high dimensional
- dimensionality reduction