Rock deformation is considered one of the essential rock properties used in designing and constructing rock-based structures, such as tunnels slopes. This study applied two well-established ensemble techniques, including boosting bagging, to artificial neural networks decision tree methods for predicting Young’s modulus material. These techniques were a dataset comprising 45 data samples from m...

Like many predictive models, random forests provide point predictions for new observations. Besides the prediction, it is important to quantify uncertainty in prediction. Prediction intervals information about reliability of predictions. We have developed a comprehensive R package, [RFpredInterval](https://CRAN.R-project.org/package=RFpredInterval), that integrates 16 methods build prediction w...

green space is counted as one of the important bases of each city, especially great cities like tehran. with considering to vast application of urban green aria, the necessity of the managing these sources is felt more day-by-day. for proper managing each source, first the inventory of that source is taken into the consideration. regretfully so far there has been no comprehensive studios patter...

In machine learning ensemble methods have demonstrated high accuracy for the variety of problems in different areas. The most known algorithms intensively used in practice are random forests and gradient boosting. In this paper we present InfiniteBoost — a novel algorithm, which combines the best properties of these two approaches. The algorithm constructs the ensemble of trees for which two pr...

The enormous growth experienced by the credit industry has led researchers to develop sophisticated credit scoring models that help lenders decide whether to grant or reject credit to applicants. This paper proposes a credit scoring model based on boosted decision trees, a powerful learning technique that aggregates several decision trees to form a classifier given by a weighted majority vote o...

Ensemble methods are able to improve the predictive performance of many base classifiers. Up till now, they have been applied to classifiers that predict a single target attribute. Given the non-trivial interactions that may occur among the different targets in multi-objective prediction tasks, it is unclear whether ensemble methods also improve the performance in this setting. In this paper, w...