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《Computer Science》 2012-09
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Visualize Black-box of NN Model and its Application in Dimensionality Reduction

YAO Li-zhong1 LI Tai-fu2 YI Jun2 SU Ying-ying2 HU Wen-jin2 XIAO Da-zhi 2(School of Electronic Engineering,Xi’an Shiyou University,Xi’an 710065,China)1(School of Electrical and Information Engineering,Chongqing University of Science and Technology,Chongqing 401331,China)2  
Since the neural network can easily fit the nonlinear mapping from the input space to the output space,the users of artificial neural network directly use it to get the black-box model with data pairs including input variables and output variables,often without taking dependencies between the input variables and output variables into account.So,there are often redundant variables in the model which would result in poor reliability and robustness.An approach to increase visual capability for black-box properties of neural network was proposed.Firstly,the network interpretation diagram is employed to make the network transparency.Then,connection weights method is used to compute the relative contribution of each input variable for estimating the importance to the output variable.Lastly,the significance tests of the connection weights and contribution ratios of input variables are implemented using the improved randomization tests for trimming the model,and the redundant variables can be eliminated by the intersection of the variables which are not significant for the overall contribution and the relative contribution rate to realize dimensionality reduction of neural network model.The experimental results indicate that the method can increase the transparency of model,select the best input variable set,eliminate redundant input variables,and improve the reliability and robustness of the model.Therefore,the study provides a new approach to visualize neural network model and eliminates redundant input variables.
【Fund】: 国家自然科学基金(61174015 51075418);; 重庆市自然科学基金(CSTC2010BB2285)资助
【CateGory Index】: TP183
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