A Novel Deep Learning Approach: Stacked Evolutionary Auto-encoder
- Publication Type:
- Conference Proceeding
- Proceedings of the International Joint Conference on Neural Networks, 2018, 2018-July
- Issue Date:
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
© 2018 IEEE. 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 prob- lems, showing that the proposed method is able to outperform existing methods with a reduced risk of overfitting the training data.
Please use this identifier to cite or link to this item: