Machine Learning
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Fuzzy polynomial neural networks for approximation of the compressive strength of concrete
Applied Soft Computing
Reduced bootstrap aggregating of learning algorithms
Pattern Recognition Letters
Knowledge discovery of concrete material using Genetic Operation Trees
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Evaluation of face recognition techniques using PCA, wavelets and SVM
Expert Systems with Applications: An International Journal
A comparative assessment of ensemble learning for credit scoring
Expert Systems with Applications: An International Journal
Mammographic Mass Detection using Wavelets as Input to Neural Networks
Journal of Medical Systems
Poly-bagging predictors for classification modelling for credit scoring
Expert Systems with Applications: An International Journal
Two credit scoring models based on dual strategy ensemble trees
Knowledge-Based Systems
Hi-index | 0.00 |
This paper investigates the use of wavelet ensemble models for high performance concrete (HPC) compressive strength forecasting. More specifically, we incorporate bagging and gradient boosting methods in building artificial neural networks (ANN) ensembles (bagged artificial neural networks (BANN) and gradient boosted artificial neural networks (GBANN)), first. Coefficient of determination (R^2), mean absolute error (MAE) and the root mean squared error (RMSE) statics are used for performance evaluation of proposed predictive models. Empirical results show that ensemble models (R^2"B"A"N"N=0.9278, R^2"G"B"A"N"N=0.9270) are superior to a conventional ANN model (R^2"A"N"N=0.9088). Then, we use the coupling of discrete wavelet transform (DWT) and ANN ensembles for enhancing the prediction accuracy. The study concludes that DWT is an effective tool for increasing the accuracy of the ANN ensembles (R^2"W"B"A"N"N=0.9397, R^2"W"G"B"A"N"N=0.9528).