A novel deep learning approach

stacked evolutionary auto-encoder

Yaoming Cai, Zhihua Cai, Meng Zeng, Xiaobo Liu, Jia Wu, Guangjun Wang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

1 Citation (Scopus)

Abstract

Deep neural networks have been successfully applied to many data mining problems in recent works. The training of deep neural networks relies heavily upon gradient descent methods, however, which may lead to the failure of training due to the vanishing gradient (or exploding gradient) and local optima problems. In this paper, we present SEvoAE method based on using Evolutionary Multiobjective optimization (EMO) algorithm to train single layer auto-encoder, and sequentially learning deeper representation in a stacking way. SEvoAE is able to achieve accurate feature representation with good sparseness by globally simultaneously optimizing two conflicting objective functions and allows users to flexibly design objective functions and evolutionary optimizers. We compare results of the proposed method with existing architectures for seven classification problems, showing that the proposed method is able to outperform existing methods with a reduced risk of overfitting the training data.
Original languageEnglish
Title of host publication2018 International Joint Conference on Neural Networks (IJCNN)
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-8
Number of pages8
ISBN (Electronic)9781509060146
ISBN (Print)9781509060153
DOIs
Publication statusPublished - 1 Jul 2018
Event2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Conference

Conference2018 International Joint Conference on Neural Networks, IJCNN 2018
CountryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

Keywords

  • data mining
  • evolutionary computation
  • gradient methods
  • learning (artificial intelligence)
  • neural nets
  • stacked Evolutionary auto-encoder
  • deep neural networks
  • data mining problems
  • gradient descent methods
  • vanishing gradient
  • local optima problems
  • SEvoAE method
  • single layer auto-encoder
  • conflicting objective functions
  • evolutionary optimizers
  • training data
  • evolutionary multiobjective optimization algorithm
  • feature representation
  • classification problems
  • deep learning approach
  • Training
  • Optimization
  • Decoding
  • Neural networks
  • Linear programming
  • Feature extraction
  • Stacking
  • Deep learning
  • Auto-encoder
  • Evolutionary Multiobjective Optimization

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