Abstract
In order to apply the ensemble learning method to the regression analysis, three new procedures "Arcing_RA1, 2, 3 (Adaptive Resampling and Combining for Regression Analysis) " are proposed. We tested their ability by using Friedman Test ; f(x) = 10sin(pix1x2) + 20(x2-0.5)2 + 10x4 + 5x5 + N(1,0) (x1-5: random number from 0 to 1, N(1,0): normal deviation ). For the training data and test data, 200 and 1000 data were generated, respectively. In the case of single three-layered Neural Network, over learning was observed when the number of neurons in hidden layer (Ny) was over 10. Therefore, the Neural Network, whose Ny was set to 5, was chosen as a weak learner. As a result of the ensemble learning using Arcing_RA1-3, the average of absolute deviation of both training and test data were decreased about 20%. In addition, over learning was not observed. From these results, it was confirmed that Arcing_RA is useful as ensemble learning method for the regression analysis.
