Feature Selection for Binary Classification Within Functional Genomics Experiments via Interquartile Range and Clustering.
Zardad KhanMuhammad NaeemUmair KhalilDost Muhammad KhanSaeed AldahmaniMuhammad HamrazPublished in: IEEE Access (2019)
Keyphrases
- binary classification
- feature selection
- multi class
- support vector
- functional genomics
- support vector machine
- cost sensitive
- class imbalance
- multi label
- learning problems
- gene expression data
- high dimensionality
- unsupervised learning
- clustering algorithm
- generalization error
- text categorization
- text classification
- machine learning
- naive bayes
- k means
- prediction accuracy
- gene expression
- kernel methods
- drug design
- classification accuracy
- data points
- dimensionality reduction
- text mining
- ensemble methods
- decision trees
- feature set
- markov random field
- classification algorithm
- high dimensional data
- kernel function
- training data
- feature extraction
- information retrieval