Sparse representation based feature selection for mass spectrometry data.
Jiqing KeLei ZhuBin HanQi DaiYaojia WangLihua LiShenhua XuHangzhou MouZhiguo ZhengPublished in: BIBM Workshops (2010)
Keyphrases
- mass spectrometry data
- feature selection
- cancer classification
- pattern classification
- high throughput
- mass spectrometry
- gene selection
- sparse representation
- text categorization
- text classification
- microarray
- machine learning
- feature extraction
- random forest
- microarray data
- feature selection algorithms
- feature set
- gene expression data
- support vector
- information gain
- high dimensionality
- classification accuracy
- feature ranking
- gene expression profiles
- feature subset
- data sets
- gene expression
- image classification
- microarray datasets
- multi class
- knn