Hybrid Neural Network mixed with Random Forests and Perlin noise - Mengfei Wang; Fengqin Zhang; Hua Guan; Xiaoqing Li; Guirong Chen; Tengyao Li; Xi Xi (2016 2nd IEEE International Conference on Computer and Communications (ICCC), 2016)
Data mixed up with discrete and continuous features makes negative affect to the classification for existing models which discretizes the continuous features or even without any treatment is not able to deal with it. The noise which is not subject to Gaussian distribution also severely affected the result of classification. In this paper, a deep neural network mixed with random forests, Stacked Denoising Autoencoder and Multilayer Perception is proposed to improve the classification effect of mixed features input. Furthermore, to improve pre-training effect of the Stacked Denoising Autoencoder, Perlin noise is added to the Gaussian Noise in noise layer. A certain promotion of this method has been proved through the experiments with the crime data between 2003 and 2015 in San Francisco, and comparisons by 10-fold cross-validation with other common methods of classification shows that this method has advantages on data mixed up with discrete and continuous features.