Stacking ensemble modelingRiver flowPredictionMonthly river flow forecasting has a vital role in many water resource management activities, especially in extreme events such as flood and drought. Therefore, experts need a reliable and precise model for forecasting. The ensemble machine learning (EML) ...
The overall results of the meta-learner (i.e., stack ensemble) have revealed performance rise over the base learners with TF-IDF character n-grams. Keywords: ensemble learning; Amharic sentiment classification; stacking; meta-learner; character n-grams...
Instead, stacking introduces the concept of a metalearner […] Stacking tries to learn which classifiers are the reliable ones, using another learning algorithm—the metalearner—to discover how best to combine the output of the base learners. — Page 497,Data Mining: Practical Machine Learning T...
Paper tables with annotated results for Learning to Update for Object Tracking with Recurrent Meta-learner
It is a highly innovative and fully automated active security tool which uses the LSTM meta-learner algorithm. This method can learn to use a small number of samples for effective classification. At the same time, it can converge in very few steps. Inspired by the LSTM model, the proposed ...
Output: Meta-learner For i= 1 to N Create a subset of U named Ui containing only the feature,i End For For i= 1 to N Train the base classifier Bi from Ui andL Compute Wi, the performance of the classifierBi, using cross-validation End For For each drug pair, d inL∪{U} For ...
A ResNet is constructed by stacking a number of residual building blocks together. Restricted Probabilistic Model Fulfilment (RPMF). RPMF is a reachability repair algorithm that enables B models to achieve given goal states. Semantic Learning …...
An advanced meta-learner based on artificial electric field algorithm optimized stacking ensemble techniques for enhancing prediction accuracy of soil shear strengthSoil shear strengthEnsemble modelMultivariate adaptive regression splinesNeural networkStacking...
This research presents a new model of stacking ensemble learning, which combines optimized base learner algorithms after applying hyperparameter tuning and the voting model to the stacking metalearner algorithm. The research compares various base machine learning models,...
The detection performance of a single weak learner is poor; however, the proper composition of several weak learners builds a strong meta-learner (Rätsch et al., 2001; Chen and Guestrin, 2016). Similar to bagging, the meta-level output is obtained through majority voting of the outputs of...