A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data

Table 2

Feature selection methods applied on microarray data.

Method	Type	Supervised	Linear	Description
-test feature selection [49]	Filter	—	Yes	It finds features with a maximal difference of mean value between groups and a minimal variability within each group
Correlation-based feature selection (CFS) [50]	Filter	—	Yes	It finds features that are highly correlated with the class but are uncorrelated with each other
Bayesian networks [51, 52]	Filter	Yes	No	They determine the causal relationships among features and remove the ones that do not have any causal relationship with the class
Information gain (IG) [53]	Filter	No	Yes	It measures how common a feature is in a class compared to all other classes
Genetic algorithms (GA) [33, 54]	Wrapper	Yes	No	They find the smaller set of features for which the optimization criterion (classification accuracy) does not deteriorate
Sequential search [55]	Wrapper	—	—	Heuristic base search algorithm that finds the features with the highest criterion value (classification accuracy) by adding one new feature to the set every time
SVM method of recursive feature elimination (RFE) [30]	Embedded	Yes	Yes	It constructs the SVM classifier and eliminates the features based on their “weight” when constructing the classifier
Random forests [41, 56]	Embedded	Yes	Yes	They create a number of decision trees using different samples of the original data and use different averaging algorithms to improve accuracy
Least absolute shrinkage and selection operator (LASSO) [57]	Embedded	Yes	Yes	It constructs a linear model that sets many of the feature coefficients to zero and uses the nonzero ones as the selected features.

Different feature selection methods and their characteristics.