The random forest (RF) algorithm has several hyperparameters that have to be set by the
user, for example, the number of observations drawn randomly for each tree and whether …

Ensemble learning is a prolific field in Machine Learning since it is based on the assumption
that combining the output of multiple models is better than using a single model, and it …

Random forest is one of the most widely used machine learning algorithms. Decision trees
used to construct the random forest may have low classification accuracies or high …

Ensemble learning has gained success in machine learning with major advantages over
other learning methods. Bagging is a prominent ensemble learning method that creates …

Convolutional neural networks (CNNs) have shown remarkable performance in various real-
world applications. Unfortunately, the promising performance of CNNs can be achieved only …

Abstract ATTED-II (https://atted. jp) is a gene coexpression database for nine plant species
based on publicly available RNAseq and microarray data. One of the challenges in …

Biochar application to croplands has been proposed as a potential strategy to decrease
losses of soil‐reactive nitrogen (N) to the air and water. However, the extent and spatial …

Background The random forests algorithm is a type of classifier with prominent universality,
a wide application range, and robustness for avoiding overfitting. But there are still some …

In solving many real-world optimization problems, neither mathematical functions nor
numerical simulations are available for evaluating the quality of candidate solutions. Instead …

Supplement to “Optimal weighted nearest neighbour classifiers”. We complete the proof of
Theorem 1, and give the proofs of the other results in the paper. We also discuss minimax …