Initialization of MLP Parameters Using Deep Belief Networks for Cancer Classification

Deep belief network (DBN) is deep neural network structure consisting of a collection of restricted Boltzmann machine (RBM). RBM is two-layered simple neural networks which are formed by a visible and hidden layer, respectively. Each visible layer receives a lower-level feature set learned by previous RBM and passes it through to top layers turning them into a more complex feature structure. In this study, the proposed method is to feed the training parameters learned by DBN to multilayer perceptron as initial weights instead of starting them from random points. The obtained results on the bioinformatics cancer dataset show that using initial weights trained by DBN causes more successful classification results than starting from random parameters. The test accuracy using proposed method increased from 77.27 to 95.45%. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.