The Research Of Classification Method Of Tumor Data Based On BP Neural Network

To identify different subtypes of the tumor from the perspective of tumor gene data,not only with traditional morphological tumor were compared to identify,but also can understand the mechanism of tumor production,ultimately provide positive recommendations for diagnosis of pathological changes of tumor;it is one of the key research contents of tumor gene data to analyze the treatment of tumor gene data,and then use the pattern recognition method to judge the benign and malignant type of tumor.Aimed at the characteristics of multidimensional features of tumor gene data sets and the difficulty of its classification effect,a method of classification of tumor data based on back propagation(BP)neural network is proposed in this dissertation.The emphasis of this method is as follows:1)How to extract the noise data and redundant feature set efficiently and extract a small and valuable gene set as the characteristic input of BP neural network(BPNN),thus simplifying the input structure of neural network and speeding up the convergence rate.2)How to avoid BPNN in the optimization of network weight and threshold process is easily limited to the extreme value of the situation.In order to solve the first problem,a compound feature selection method based on random forest and neighborhood rough set(RFNRS)is proposed in this dissertation.The noise data and the set of redundant feature attributes in the original high-dimensional feature set are screened from the perspective of feature selection.First,the Relief series of algorithm is used to preliminarily process the tumor gene data set,and then the feature of the redundant feature is deleted by using the encapsulated feature algorithm based on random forest attribute set,and finally obtains the simplest and optimal set by forward search of neighborhood rough set.After six experiments with tumor gene data,the feature selection method can quickly filter the irrelevant attribute set,recognition accuracy and processing rate after ten-fold cross-validation have been effectively improved;In order to solve the second problem,this dissertation introduces the Mind Evolutionary Algorithm(MEA)in the artificial intelligence algorithm to construct the MEA-BPNN,and uses the similartaxis operator in the MEA to find the best individual solution in the subgroup and uses the dissimilation operator to guarantee the best individual solution in global space.The results show that the classification accuracy of MEA-BPNN is superior to the traditional BPNN and GA-BPNN algorithm on different tumor gene data sets.In summary,according to the difficulties of processing the tumor gene data set and the deficiency of BPNN recognition modeling,this dissertation proposed a compound feature selecting method that is named RFNRS and introduced an intelligent algorithm which is called MEA.On the one hand,RFNRS can quickly select the appropriate oncogene,reduce the network training time of BPNN,enhance the learning efficiency of BPNN,and thus improve its recognition accuracy;On the other hand,the network weights and thresholds of BPNN are improved by MEA,which further improves the classification performance of BPNN.This method has a certain degree of theoretical guidance and practical application value in the recognition of tumor gene data sets.