With
respect to the different specific problems, the prediction accuracy of
traditional machine learning methods often exist difference, while ensemble
learning achieves significant improvement in classification performance by
combining several of base classifiers. First, the basic idea of ensemble
learning is briefly introduced, and the advantages of Stacking over the
traditional classical ensemble algorithms are analyzed. Then, based on the
Stacking framework, the two-layer classification model is developed to evaluate
the personal credit by using the UCI datasets. Finally, the proposed method is
applied to the empirical analysis, and the results show that compared with the
single machine learning method of SVM, RF, ANN, GBDT and simple average
ensemble, Stacking with two-layer
classifier has a
better prediction effect.
