Assuming a view of the Random Forest as a special case of a nested ensemble of interchangeable modules, we construct a generalisation space allowing one to easily develop novel methods based on this algorithm. We discuss the role and required properties of modules at each level, especially in context of some already proposed RF generalisations.

In this paper, we propose an improved random forest algorithm which allocates weights to decision trees in the forest during tree aggregation for prediction and their weights are easily calculated based on out-of-bag errors in training. Experiments results show that our proposed algorithm beats the original random forest and other popular classification algorithms such as SVM, KNN and C4.5 in t...

Based on the research of predicting β-hairpin motifs in proteins, we apply Random Forest and Support Vector Machine algorithm to predict β-hairpin motifs in ArchDB40 dataset. The motifs with the loop length of 2 to 8 amino acid residues are extracted as research object and the fixed-length pattern of 12 amino acids are selected. When using the same characteristic parameters and the same test me...

Random forests are a popular classification method based on an ensemble of a single type of decision trees from subspaces of data. In the literature, there are many different types of decision tree algorithms, including C4.5, CART, and CHAID. Each type of decision tree algorithm may capture different information and structure. This paper proposes a hybrid weighted random forest algorithm, simul...

The Random Forest algorithm is an ensemble technique that can achieve high accuracy on classification and regression with minimal tuning of parameters. This paper analyzes the effectiveness of the Random Forest classification algorithm under decreasing randomness in the bootstrap sampling procedure, in increasing tournament size, and in tournament participant selection.

Classical methods for classification of pixels in multispectral images include supervised classifiers such as the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector machines, and decision trees. Recently, there has been an increase of interest in ensemble learning – a method that generates many classifiers and aggregates their results. Breiman propo...

Unlike traditional pilot reports, in-situ EDR reports of atmospheric turbulence from commercial aircraft contain both positive and negative instances, are reported regularly, and have relatively accurate positions and timestamps. These data therefore make it feasible to perform more sophisticated analyses of the causes of atmospheric turbulence than were formerly possible. Several real-time gri...