| Study | Ensemble type | Base learner(s) | Combination rule(s) | Number of datasets |
|
Braga et al. [22] | Homogeneous (bagging) | Linear regression | Linear (averaging) |
1 | Homogeneous (bagging) | MLP | Linear (averaging) | Homogeneous (bagging) | M5P regression trees | Linear (averaging) | Homogeneous (bagging) | M5P model trees | Linear (averaging) | Homogeneous (bagging) | SVR | Linear (averaging) |
| Kultur et al. [23, 24] | Homogeneous (bootstrapping) | MLP | Nonlinear (average of largest cluster obtained using adaptive resonance theory (ART) algorithm) | 5 |
|
Minku and Yao [25, 26] | Homogeneous (bagging, random, negative correlation learning) | MLP | Linear (averaging) |
18 | Homogeneous (bagging) | RBF | Linear (averaging) | Homogeneous (bagging) | Regression Trees | Linear (averaging) |
| Kocaguneli et al. [27] | Heterogeneous | Gaussian process, MLP, RBF, SMOReg, SVMReg, IBk, LWL, additive regression, bagging with decision tree, RandomSubSpace, DecisionStump, M5P, ConjunctiveRule, DecisionTable | Linear (averaging) | 3 |
| Kocaguneli et al. [28] | Heterogeneous | ABE0-1NN, ABE0-5NN, SWReg, CART (yes), CART (no), NNet, LReg, PCR, PLSR | Linear (mean, median, inverse-ranked weighted mean (IRWM)) | 20 |
| Elish [29] | Heterogeneous | MLP, RBF, RT, KNN, SVR | Linear (median) | 5 |
| | Homogeneous (bagging) | MLP | Linear (averaging, weighted averaging) and nonlinear (MLP SVR, FIS-FCM, FIS-SC, ANFIS-FCM, ANFIS-SC) | | This paper | Homogeneous (bagging) | SVR |
5 | Homogeneous (bagging) | ANFIS | | Heterogeneous | MLP, SVR, ANFIS | |
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