Bagging Ensemble Selection for Regression
نویسندگان
چکیده
Bagging ensemble selection (BES) is a relatively new ensemble learning strategy. The strategy can be seen as an ensemble of the ensemble selection from libraries of models (ES) strategy. Previous experimental results on binary classification problems have shown that using random trees as base classifiers, BES-OOB (the most successful variant of BES) is competitive with (and in many cases, superior to) other ensemble learning strategies, for instance, the original ES algorithm, stacking with linear regression, random forests or boosting. Motivated by the promising results in classification, this paper examines the predictive performance of the BES-OOB strategy for regression problems. Our results show that the BES-OOB strategy outperforms Stochastic Gradient Boosting and Bagging when using regression trees as the base learners. Our results also suggest that the advantage of using a diverse model library becomes clear when the model library size is relatively large. We also present encouraging results indicating that the non-negative least squares algorithm is a viable approach for pruning an ensemble of ensembles.
منابع مشابه
Application of ensemble learning techniques to model the atmospheric concentration of SO2
In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single base classifiers such as support vector machine, multilayer perceptron, linear regression and re...
متن کاملRandom Subspacing for Regression Ensembles
In this work we present a novel approach to ensemble learning for regression models, by combining the ensemble generation technique of random subspace method with the ensemble integration methods of Stacked Regression and Dynamic Selection. We show that for simple regression methods such as global linear regression and nearest neighbours, this is a more effective method than the popular ensembl...
متن کاملEnsemble of M5 Model Tree Based Modelling of Sodium Adsorption Ratio
This work reports the results of four ensemble approaches with the M5 model tree as the base regression model to anticipate Sodium Adsorption Ratio (SAR). Ensemble methods that combine the output of multiple regression models have been found to be more accurate than any of the individual models making up the ensemble. In this study additive boosting, bagging, rotation forest and random subspace...
متن کاملIntegrating Instance Selection and Bagging Ensemble using a Genetic Algorithm
Ensemble classification combines individually trained classifiers to obtain more accurate predictions than individual classifiers alone. Ensemble techniques are very useful for improving the generalizability of the classifier. Bagging is the method used most commonly for constructing ensemble classifiers. In bagging, different training data subsets are drawn randomly with replacement from the o...
متن کاملComparison of Ensemble Strategies in Online NIR for Monitoring the Extraction Process of Pericarpium Citri Reticulatae Based on Different Variable Selections.
Different ensemble strategies were compared in online near-infrared models for monitoring active pharmaceutical ingredients of Traditional Chinese Medicine. Bagging partial least square regression and boosting partial least square regression were adopted to near-infrared models, to determine hesperidin and nobiletin content during the extraction process of Pericarpium Citri Reticulatae in a pil...
متن کامل