Stacking and rotation-based technique for machine learning classification with data reduction

The paper focuses on using stacking and rotation-based technique to improve performance and generalization ability of the machine learning classification with data reduction. The aim of data reduction technique is decreasing the quantity of information required to learn a high quality classifiers, especially when the data are huge. The paper shows that merging both stacking and rotation-based ensemble techniques with machine classification based on data reduction may bring additional benefits with respect to the accuracy of the classification process. The finding that has been confirmed by computational experiments. The paper includes the description of the approach and the discussion of the computational experiment results.