… In this paper, heterogeneous ensemble of classifiers is evaluated and the outputs are … by
a boosting meta-learner. Both ADABOOST and XGBOOST are tried for the meta-learning stage, …

… heterogeneous ensemble system where each learning algorithm uses a different learning
… Whereas features in the original data often differ in scale and type, meta-data, which can be …

… heterogeneous ensembles, comprised of fundamentally different model types. Heterogeneous
ensembles … stream ensembles to weight the votes of (heterogeneous) ensemble members …

… In this work, two different learning algorithms were considered as meta-classifiers, namely,
MLR and M5',73 but the experimental results indicate than M5' outperforms MLR slightly. …

… with a worse performance are those in which the outputs of base learners are combined
using a Bayesian meta-classifier. On the other hand, when the datasets are affected by labelling …

… We study the use of heterogeneous ensembles for data streams… In the data stream setting,
meta-learning techniques are … , effectively creating a heterogeneous ensemble (albeit at a …

… of an ensemble, the heterogeneous ensemble can improve … metalearners in stacked
ensembles, the base learners are … The classification learning algorithms used as base learners …

… , unlike prevailing meta-learning-based IL solutions, we decouple the model-training and
meta-training in MESA by independently train the meta-sampler over task-agnostic meta-data. …

… This paper proposed an optimized heterogeneous stacking ensemble model … heterogeneous
pre-trained DL models including RNN, LSTM, and GRU. We explore three meta-learners …

… In this scenario, we can have individual that represent homogeneous and heterogeneous
ensembles of different sizes. 3) Equal: It generates a an initial population where each …