Ensemble model through mixed projections useful for big data analytics

In this paper, we propose mixed projection forest (MPF), a new classification ensemble method that can be effectively applied in the field of big data analysis.When training individual classifiers within an ensemble, MPF uses oblique hyperplanes using combined rotation matrix derived from data projection techniques of principal component analysis (PCA) and canonical linear discriminant analysis (CLDA), thereby improving the accuracy of each classifier.Additionally, the diversity of individual classifiers is improved by generating various rotation matrices through random partitioning of the input variable set.This approach ultimately enhances classification performance and proves to be highly effective in big data analysis that demands precision.We conducted a performance comparison of MPF with existing classification ensemble models using 30 real or simulated datasets.The results indicate that MPF achieves competitive performance in terms of classification accuracy and classifier diversity.