Random Forest (RF) is one of the most successful strategies for automated classification tasks. Motivated by the RF success, recently proposed RF-based classification approaches leverage the central RF idea of aggregating a large number of low-correlated trees, which are inherently parallelizable and provide exceptional generalization capabilities. In this context, this work brings several new contributions to this line of research. First, we propose a new RF-based strategy (BERT) that applies the boosting technique in bags of extremely randomized trees. Second, we empirically demonstrate that this new strategy, as well as the recently proposed BROOF and LazyNN_RF classifiers do complement each other, motivating us to stack them to produce an even more effective classifier. Up to our knowledge, this is the first strategy to effectively combine the three main ensemble strategies: stacking, bagging (the cornerstone of RFs) and boosting. Finally, we exploit the efficient and unbiased stacking strategy based on out-of-bag (OOB) samples to considerably speedup the very costly training process of the stacking procedure. Our experiments in several datasets covering two high-dimensional and noisy domains of topic and sentiment classification provide strong evidence in favor of the benefits of our RF-based solutions. We show that BERT is among the top performers in the vast majority of analyzed cases, while retaining the unique benefits of RF classifiers (explainability, parallelization, easiness of parameterization). We also show that stacking only the recently proposed RF-based classifiers and BERT using our OOB-based strategy is not only significantly faster than recently proposed stacking strategies (up to six times) but also much more effective, with gains up to 21% and 17% on MacroF1 and MicroF1, respectively, over the best base method, and of 5% and 6% over a stacking of traditional methods, performing no worse than a complete stacking of methods at a much lower computational effort.

The task of retrieving information that really matters to the users is considered hard when taking into consideration the current and increasingly amount of available information. To improve the effectiveness of this information seeking task, systems have relied on the combination of many predictors by means of machine learning methods, a task also known as learning to rank (L2R). The most effective learning methods for this task are based on ensembles of tress (e.g., Random Forests) and/or boosting techniques (e.g., RankBoost, MART, LambdaMART). In this paper, we propose a general framework that smoothly combines ensembles of additive trees, specifically Random Forests, with Boosting in a original way for the task of L2R. In particular, we exploit out-of-bag samples as well as a selective weight updating strategy (according to the out-of-bag samples) to effectively enhance the ranking performance. We instantiate such a general framework by considering different loss functions, different ways of weighting the weak learners as well as different types of weak learners. In our experiments our rankers were able to outperform all state-of-the-art baselines in all considered datasets, using just a small percentage of the original training set and faster convergence rates.