… In this paper, we study the ensembles with the weighting combination rule. Metalearning …
analysis of linear combiners for multiple classifier systems,” IEEE Trans. Pattern Anal. Mach. …

… a stochastic distribution of the training datasets, where small changes in the training data set
will lead to … several baseline deep models and combining some rules to make predictions. …

… A comprehensive review of multiple classifier systems based on the … according to the
following rule: … If E is small and acceptable, the ensemble architecture is believed to have the …

… 132 from the individual learners are combined using a combi- 133 nation rule to obtain a …
In terms 542 of disadvantages, the LightGBM can overfit small training 543 datasets easily as it …

… ensemble learning models combine the advantages of both the deep learning models as well
as the ensemble learning … This paper reviews the state-of-art deep ensemble models and …

… the classifier’s model relative to features with relatively small … different classification algorithms
among five preprocessed sets … based ensemble classifier with AOP combination rule is …

… Nevertheless, small proportion of researchers have employed the power of … his proposed
rule induction algorithm known as RISE [21]. In this study, the training set was segregated into …

… is done through stochastic gradient descent over shuffled mini-batches with the adadelta
update rule. In SVM and NB-SVM we use linear kernel. Since feature engineering is not the …

… learners and are therefore directly affected by the intrinsic limitations of each learning algorithm…
, and combine multiple learners to build ensemble learners that increase the accuracy of …

… classification algorithm which combines a set of cost-sensitive multi-class weak learners to
obtain a strong classification rule … to replace zero with a very small positive number such as 1 …