Table 1: Summary of ensembles.

Optimization levelEnsemble learning phaseEnsemble levelStrategy adoptedMethod employed

Decision optimization Ensemble integration Combination levelFusionMajority voting method [7072]
Threshold plurality vote method [73]
Naïve Bayes method [74, 75]
Fuzzy theory method [76, 77]
Decision template method [78]
Metalearning method [79]
Hierarchically structured method [82, 83]
Boolean combination method [2]
SelectionThe test and select method [71]
Cascading classifiers method [85]
Dynamic classifier selection method [86, 87]
Clustering-based selection method [17, 45, 88, 91]
Statistical selection method [89]
Mixture of expert systemsStochastic selection method [46]
Winner-takes-all method [46]
Weighting method [46]

Coverage optimization Ensemble selection Classifier levelHomogenous Clustering-based selection method [17, 45, 88, 91]
Threshold-based selection method [86]
Heterogeneous
Ensemble generationFeature levelFeature selection/reduction Random subspace method [46]
The input decimation method [90]
Genetic algorithms [92]
Markov blanket BN [28]
Principal component analysis [93]
Information theory [16]
Data levelResamplingBagging [61]
Wagging [94]
Random forest [95]
Boosting [96]
Stacking [79]
Output code methodOne per class (OPC) [97]
Pairwise coupling [98]
Correcting classifiers [99]
Pairwise coupling correcting classifiers [99]
Error-correcting output coding [100]
Data-driven ECOC [101]