Ensemble models combine two or more models to enable a more robust prediction, classification, or variable selection. This paper describes three types of ensemble models: boosting, bagging, and model averaging. It discusses go-to methods, such as gradient boosting and random forest, and newer methods, such as rotational forest and fuzzy clustering. The examples section presents a quick setup th...

We present the ideas and methodologies that we used to address the KDD Cup 2009 challenge on rank-ordering the probability of churn, appetency and up-selling of wireless customers. We choose stochastic gradient boosting tree (TreeNet R ) as our main classifier to handle this large unbalanced dataset. In order to further improve the robustness and accuracy of our results, we bag a series of boos...

One of the most effective methods for text classification is the recently proposed BROOF classifier, a boosted version of Random Forest (RF). In this work, we propose to improve the BROOF strategy by exploiting Extremely Randomized Trees (Extra-Trees) as a “weak learner” in the boosting framework. In this context, we also introduce the Bagging procedure into the Extra-Trees models so that we ca...

We present a learning framework for structured support vector models in which boosting and bagging methods are used to construct ensemble models. We also propose a selection method which is based on a switching model among a set of outputs of individual classifiers when dealing with natural language parsing problems. The switching model uses subtrees mined from the corpus and a boosting-based a...

An accurate reservoir characterization is a crucial task for the development of quantitative geological models and reservoir simulation. In the present research work, a novel view is presented on the reservoir characterization using the advantages of thin section image analysis and intelligent classification algorithms. The proposed methodology comprises three main steps. First, four classes of...

Credit scoring is an effective tool for banks and lending companies to manage the potential credit risk of borrowers. Machine learning algorithms have made grand progress in automatic accurate discrimination good bad Notably, ensemble approaches are a group powerful tools enhance performance scoring. Random forest (RF) Gradient Boosting Decision Tree (GBDT) become mainstream methods precise RF ...

Considerable research e1ort has been expended to identify more accurate models for decision support systems in &nancial decision domains including credit scoring and bankruptcy prediction. The focus of this earlier work has been to identify the “single best” prediction model from a collection that includes simple parametric models, nonparametric models that directly estimate data densities, and...

Effective exploitation of overhead transmission lines needs reliable and precise dynamic line rating forecasting. High-accuracy forecasting, in particular, is an important short-term method for coping with grid congestion, enhancing stability, accommodating high renewable energy penetration. Due to the non-stationarity stochasticity meteorological variables, a single model often not sufficient ...