Adaptive Structural Learning of Deep Belief Network for Medical Examination Data and Its Knowledge Extraction by using C4.5

by   Shin Kamada, et al.
Prefectural University of Hiroshima

Deep Learning has a hierarchical network architecture to represent the complicated feature of input patterns. The adaptive structural learning method of Deep Belief Network (DBN) has been developed. The method can discover an optimal number of hidden neurons for given input data in a Restricted Boltzmann Machine (RBM) by neuron generation-annihilation algorithm, and generate a new hidden layer in DBN by the extension of the algorithm. In this paper, the proposed adaptive structural learning of DBN was applied to the comprehensive medical examination data for the cancer prediction. The prediction system shows higher classification accuracy (99.8 traditional DBN. Moreover, the explicit knowledge with respect to the relation between input and output patterns was extracted from the trained DBN network by C4.5. Some characteristics extracted in the form of IF-THEN rules to find an initial cancer at the early stage were reported in this paper.


page 1

page 2

page 3

page 4


An Adaptive Learning Method of Deep Belief Network by Layer Generation Algorithm

Deep Belief Network (DBN) has a deep architecture that represents multip...

Knowledge Extracted from Recurrent Deep Belief Network for Real Time Deterministic Control

Recently, the market on deep learning including not only software but al...

Please sign up or login with your details

Forgot password? Click here to reset