Chinese Journal on Internet of Things ›› 2018, Vol. 2 ›› Issue (2): 65-72.doi: 10.11959/j.issn.2096-3750.2018.00055

• Theory and Technology • Previous Articles     Next Articles

Multilayer neural network model for unbalanced data

Xue ZHANG,Zhiguo SHI,Xuan LIU   

  1. School of Computer &Communication Engineering,University of Science and Technology Beijing,Beijing 100083,China
  • Revised:2018-05-15 Online:2018-06-01 Published:2018-07-03
  • Supported by:
    The National Key R&D Program of China(2016YFC0901303)

Abstract:

Classification of unbalanced data often has low performance of the classifier because of the unbalance of data between classes.Using AUC (the area under the ROC curve) as evaluation index,combined with one class F-score feature selection and genetic algorithm,a multilayer neural network model was established,and a more favorable feature set for unbalanced data classification was selected,so as to establish a deeper model suitable for classification of unbalanced data.Based on Tensor Flow,a multilayer neural network model was established.Using four different UCI datasets for testing,and comparing with the traditional machine learning algorithms such as Naive Bayesian,KNN,neural networks,etc,the performance of the proposed model built on the unbalanced data classification is more excellent.

Key words: unbalanced data, one class F-score feature selection, genetic algorithm, multilayer neural network

CLC Number: 

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