Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction - Erdal, Halil Ibrahim (Engineering applications of artificial intelligence, 2013)
Accurate prediction of high performance concrete (HPC) compressive strength is very important issue. In the last decade, a variety of modeling approaches have been developed and applied to predict HPC compressive strength from a wide range of variables, with varying success. The selection, application and comparison of decent modeling methods remain therefore a crucial task, subject to ongoing researches and debates. This study proposes three different ensemble approaches: (i) single ensembles of decision trees (DT) (ii) two-level ensemble approach which employs same ensemble learning method twice in building ensemble models (iii) hybrid ensemble approach which is an integration of attribute-base ensemble method (random sub-spaces RS) and instance-base ensemble methods (bagging Bag, stochastic gradient boosting GB). A decision tree is used as the base learner of ensembles and its results are benchmarked to proposed ensemble models. The obtained results show that the proposed ensemble models could noticeably advance the prediction accuracy of the single DT model and for determining average determination of correlation, the best models for HPC compressive strength forecasting are GB–RS DT, RS–GB DT and GB–GB DT among the eleven proposed predictive models, respectively. The obtained results show that the proposed ensemble models could noticeably advance the prediction accuracy of the single DT model and for determining determination of correlation (R2max), the best models for HPC compressive strength forecasting are GB–RS DT (R2=0.9520), GB–GB DT (R2=0.9456) and Bag–Bag DT (R2=0.9368) among the eleven proposed predictive models, respectively. The schematic illustration of the hybrid ensemble structure consisting of the gradient boosting method in the first level and the random subspace method in the second level. [Display omitted] •Bagging, boosting and random sub-spaces are incorporated in building DT ensembles.•The study proposes 3 ensemble modeling approaches (i.e., single, two-level, hybrid).•A DT model is employed as the base learner and benchmark model.•The obtained results of DT ensembles are superior to the DT model.•It is found that hybrid and two-level ensemble approaches are promising techniques.