Abstract. In his original paper on random forests, Breiman proposed two different decision tree ensembles: one generated from “orthogonal” trees with thresholds on individual features in every split, and one from “oblique” trees separating the feature space by randomly oriented hyperplanes. In spite of a rising interest in the random forest framework, however, ensembles built from orthogonal tr...

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.

Tree species diversity is a key parameter to describe forest ecosystems. It is, for example, important for issues such as wildlife habitat modeling and close-to-nature forest management. We examined the suitability of 8-band WorldView-2 satellite data for the identification of 10 tree species in a temperate forest in Austria. We performed a Random Forest (RF) classification (object-based and pi...

Abstract The random forest(RF) algorithm is a very efficient and excellent ensemble classification algorithm. In this paper, we improve the forest propose an called ‘Bayesian Weighted Random Forest’(B-RF), focus on problem that inaccurate decision tree caused by same voting weights in traditional model. main idea underlying proposed model to replace supermajority of forests into weighted voting...

Many mass spectrometry-based studies, as well as other biological experiments produce cluster-correlated data. Failure to account for correlation among observations may result in a classification algorithm overfitting the training data and producing overoptimistic estimated error rates and may make subsequent classifications unreliable. Current common practice for dealing with replicated data i...

The present study has proposed three novel hybrid models by integrating traditional ensemble models, such as random forest, logitboost, and naive bayes, six newly developed of rotation forest (RF), decision tree (RF-DT), J48 (DF-J48), bayes (RF-NBT), neural network (RF-NN), M5P (RF-M5P) REPTree (RF-REPTree), with statistical i.e. weight evidence, logistic regression combination WOE LR. To predi...

The Random Forest (RF) algorithm by Leo Breiman has become a standard data analysis tool in bioinformatics. It has shown excellent performance in settings where the number of variables is much larger than the number of observations, can cope with complex interaction structures as well as highly correlated variables and returns measures of variable importance. This paper synthesizes ten years of...

Motivation Genome sequencing is producing an ever-increasing amount of associated protein sequences. Few of these sequences have experimentally validated annotations, however, and computational predictions are becoming increasingly successful in producing such annotations. One key challenge remains the prediction of the amino acids in a given protein sequence that are involved in protein-protei...

Protein-protein interaction (PPI) is a combining two or more protein because of biochemical events in any living cell. Protein domains are functional and/or structure units in a protein and consequently they are responsible for protein-protein interaction. Many machine-learning approaches with domain-based models for protein interaction prediction and their feasibility are showed. In this study...

Studies designed to discriminate different successional forest stages play a strategic role in forest management, forest policy and environmental conservation in tropical environments. The discrimination of different successional forest stages is still a challenge due to the spectral similarity among the concerned classes. Considering this, the objective of this paper was to investigate the per...