Sentence examples for model tree algorithm from inspiring English sources

In this study, the M5′ model tree algorithm was used to predict the elastic modulus of recycled aggregate concrete.

The results demonstrate the strength of M5′ model tree algorithm in predicting the wave runup with high precision.

In this study, M5P model tree algorithm was used to predict the compressive strength of normal concrete (NC) and high performance concrete (HPC).

Classification accuracies from J48 algorithm, Best first tree algorithm, random forest tree algorithm, functional tree algorithm and linear model tree algorithm are compared and the best algorithm for such a system is suggested.

Empirical results showed that the ICEEMDAN-PSO-SVR model performed well for all forecasting horizons, outperforming the alternative comparison approaches: ICEEMDAN-MARS and ICEEMDAN-M5 model tree and the PSO-SVR, PSO-MARS and PSO-M5 model tree algorithm.

In this paper, which can be regarded as an extension of Pourzangbar et al. (2016), two soft computing methods, a support vector regression (SVR), and a model tree algorithm (M5′), have been implemented to predict the maximum scour depth due to non-breaking waves.

The performance of the ICEEMDAN-PSO-SVR technique was compared with alternative approaches: ICEEMDAN-multivariate adaptive regression spline (MARS) and ICEEMDAN-M5 model tree, as well as traditional modelling approaches: PSO-SVR, MARS and M5 model tree algorithms.

The main advantages of the model tree algorithms are: (a) they output relatively simple mathematical models (formulas) and (b) are more convenient to develop and employ compared with other soft computing methods.

Results indicated that non-linear regression analysis, artificial neural network, support vector machine, and model tree algorithms can predict the splitting tensile strength of concretes made with and without steel fiber reinforcement with satisfactory accuracy.

In the case of model tree algorithms, the most widespread measures are: root mean-squared error (RMSE, equation 2), mean absolute error (MAE, equation 3), and correlation coefficient [ 24].

This model is trained using the locally linear model tree (LOLIMOT) algorithm.