Skip to main content

Table 1 Groups of feature selection algorithms

From: SDPSO: Spark Distributed PSO-based approach for feature selection and cancer disease prognosis

Group Group description
Filters Use independent techniques to select features. The set of features is chosen by an evaluation criterion, or a score to assess the degree of relevance of each characteristic [10]
Wrappers The wrappers are feature selection methods that evaluate a subset of characteristics through its classification performance using a learning algorithm. The evaluation is achieved using a classifier that estimates the relevance of a given subset of characteristics [11]
Embedded Embedded methods combine the qualities of filter and wrapper methods. As the filter methods have proved to be faster yet not very efficient while the wrapper methods have proved to be more effective but very computationally expensive especially with big datasets, a solution that combines the advantages of both methods was needed
Hybrid A feature selection method that applies multiple conjuncted primary feature selection approaches consecutively [12]
Ensemble Ensemble methods aggregate groups of gene sets of diverse base classifiers. It consists of the use of different feature subsets, or so-called ensemble feature selection [13]
Integrative Integrate external knowledge for gene selection [14]