The assessment of the quantitative and qualitative changes, the result of the impacts imposed by natural factors, and human interventions during specific sampling periods has a substantial influence on nature, management method and tending operation of every region’s forests. The present research was carried out in Golestan province forests (Iran) over an 11- year period and the obtained statis...

This paper concerns an effective data mining strategy for the data set of cylinder bands in rotogravure printing using random forests. Among many data mining algorithms, random forests can be a good data mining tool for the data sets that have some bad factors for data mining like errors in data and missing data as well as irrelevant attributes. A clever strategy to utilize the property of the ...

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...

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms which also induces classification ru...

Relative importance of regressor variables is an old topic that still awaits a satisfactory solution. When interest is in attributing importance in linear regression, averaging over orderings methods for decomposing R2 are among the state-of-theart methods, although the mechanism behind their behavior is not (yet) completely understood. Random forests—a machinelearning tool for classification a...

Relevance feedback is a mechanism to interactively learn the user’s query concept online, and has been extensively used to improve the performance of multimedia information retrieval. In this paper, we present a novel interactive pattern analysis method, which reduces the relevance feedback to a two-class classification problem, and classifies multimedia objects as relevant or irrelevant. From ...

When digitizing a print bilingual dictionary, whether via optical character recognition or manual entry, it is inevitable that errors are introduced into the electronic version that is created. We investigate automating the process of detecting errors in an XML representation of a digitized print dictionary using a hybrid approach that combines rulebased, feature-based, and language modelbased ...

We present results from a large-scale empirical comparison between ten learning methods: SVMs, neural nets, logistic regression, naive bayes, memory-based learning, random forests, decision trees, bagged trees, boosted trees, and boosted stumps. We evaluate the methods on binary classification problems using nine performance criteria: accuracy, squared error, cross-entropy, ROC Area, F-score, p...