Skip to main content

Table 1 Taxonomy of FS approaches [5, 74, 75]

From: Cooperative co-evolution for feature selection in Big Data with random feature grouping

FS approaches Evaluation methods Examples of evaluator
Evaluation criteria Filter [76] Distributed FS using SC [77], information theory [78], T-test [79]
Wrapper [80] k-NN [81], SVM [82]
Embedded [83] LASSO [84], Gradient boosting [85]
Evolutionary computation EA [86] GA [87], GP [88], parallel GA [89]
CEA [25] CCEA [90]
Swarm optimization [87] PSO [87], ACO [91]
Hybrid mRMR-TLBOL [92], CMIM + BGA [93], TLBO + GSA [94]
Others ABC [95], MA [96], DE [95]
Number of objectives Single-objective [97] GA [87]
Multi-objectives [98] Nondominated sorting GA-II [99]
  1. GP genetic programming, PSO particle swarm optimization, ACO ant colony optimization, TLBO teaching learning-based algorithm, GSA gravitational search algorithm, CMIM conditional mutual information maximization, BGA binary genetic algorithm, mRMR minimum redundancy maximum relevance, TLBOL TLBO with opposition-based learning, DE differential evolution, MA memetic algorithm, LCS learning classifier system, ES evolutionary strategy, ABC artificial bee colony