Improving prediction performance using ensemble neural networks in textile sector

Neural network technique has been recently preferred in textile sector for the prediction task because the traditional mathematical and statistical methods can be inadequate to derive complex relations within textile datasets. Meanwhile ensemble learning has become a popular machine learning approach in recent years due to the high prediction performance it provides. Therefore, this study proposes an ensemble learning approach that combines neural networks with different parameter values (the number of hidden layers, learning rate and momentum coefficient) to improve prediction performance in textile sector. In the experimental studies, the proposed model was tested on ten different real-world textile datasets. The results show that ensemble neural networks usually achieve better prediction performance than an individual neural network in terms of correlation coefficient and relative absolute error measures.