Deep forest regression based on cross-layer full connection

Key process parameters such as production qualities and environmental pollution indices are difficult to be measured online in complex industrial processes. High time and economic costs make only limited small sample data be obtained to build process models, while the deep neural network model requires massive training samples. Although the deep forest algorithm is based on nonneural network structure, it mainly is utilized to effectively address classification problems. Owing to the above problems, a new deep forest regression algorithm based on cross-layer full connection is proposed. First of all, sub-forest prediction values of the input layer forest module are processed to obtain the layer regression vector, which is combined with the raw feature vector as the input of the middle layer forest model. And then, a cross-layer full connection way connecting the former layer regression vector contributes to an augmented layer regression vector. Meanwhile, the deep layer’s number is adaptively adjusted via verifying the validation error. In the end, the output layer forest model is trained by using the augmented layer regression vector originated from the middle layer forest model and the raw feature vector. Sequentially, the maximum information flow is effectively ensured by information sharing. Moreover, the proposed method has the advantages of simple hyper-parameter setting criterion. Simulation results based on benchmark and industrial data show that the proposed method has equal or better performance than several state-of-art methods.