… schemes in cross-validation, which to our knowledge has not been investigated before. …
set to generate ensemble predictions, based on which the prediction performance is evaluated. …

… prediction [4]. Thus, this study proposes a stacking ensemble classi er built upon a domain
knowledge… Implementation and performance comparison of domain knowledge-based feature …

… source domain buildings and the use of transfer knowledge when … Because each basic
prediction model is trained from its … each basic prediction model to the ensemble prediction result. …

… domains contribute equally to effective knowledge … ensemble predictions at test phase, it
becomes imperative to filter out comparatively underperforming models within the ensemble …

… In this paper, we show that if we have prior knowledge of such biases, we can train … predictions
exclusively based on dataset biases, and (2) train a robust model as part of an ensemble …

… Domain adaptation, which involves transferring knowledge from a source domain to a target
domain, … This can be represented as: θk=argminθL (θ; Dk) The ensemble prediction ypred(x) …

… , this paper considers the specific domain knowledge of PV and proposes a physics-…
predictions. This may occur in a deep neural network without incorporating domain knowledge …

… distribution of the training set and construct the prediction model. In [12], the … prediction. In
[13], the authors combined the resampling technique with ensemble learning method to predict …

… Our literature review showed that researchers collected input data based on their knowledge
of the prediction model and the availability of the data. Since the experimental condition …

… -seeking request, we utilize a binary classifier, which leverages the encoded
representation of the dialogue context produced by the DeBERTa model to make predictions. …