Ensemble Learning based on Regressor Chains: A Case on Quality Prediction

In this study we construct a prediction model, which utilize s the production process parameters acquired from a textile machine and predicts the quality characteristics of the final yarn. Several machine learning algorithms (decision tree, multivariate adaptive regression splines and random forest) are used for prediction. An ensemble method, using the idea of regressor chains, is developed to further improve the prediction performance. Collected data is first segmented into two parts (labeled as “ normal” and “unusual”) using local outlier factor method, and performance of the algorithms are tested for eac h segment separately. It is seen that ensemble idea proves its competence especially for the cases where th collected data is categorized as unusual. In such cases ensemble algorithm improves the prediction accuracy significantly.