Classification Method for Uncertain Data based on Sparse De-Noising Auto-Encoder Neural Network

Classification Method for Uncertain Data based on Sparse De-Noising Auto-Encoder Neural Network

Jijiang Yu Yuwen Huang Chunying Liu* 

Department of Computer and Information Engineering, Heze University, China, Heze 274015, Shandong

Key Laboratory of computer Information Processing, Heze University, China, Heze 274015, Shandong

Department of Computer and Information Engineering, Heze University, China, Heze 274015, Shandong

Corresponding Author Email: 
lcy810204@163.com
Page: 
210-223
|
DOI: 
https://doi.org/10.18280/ama_b.600113
Received: 
15 March 2017
| |
Accepted: 
15 April 2017
| | Citation

OPEN ACCESS

Abstract: 

Due to its importance in machine learning, pattern recognition, and many other applications, uncertain data mining has attracted much attention. This paper proposes a classification method for uncertain data based on a sparse de-noising auto-encoder neural network. Firstly, a hyper-ellipsoid convex model is used to describe the uncertain interval vector, and give an approach for uncertain data classification based on an interval uncertainty support vector machine. Secondly, this paper introduces a sparse de-noising auto encoder neural network, which can convert high-dimension data into low-dimensional characteristic space. Finally, this paper establishes a three layered auto-encoder neural network, and whose deep structure is tuned with stochastic gradient descent parameters fine-tuned by layer greedy pre-training and back propagation. Experimental results show that this proposed method creats better classification accuracy, and has stronger robustness for noise parameter, so it is effective for uncertain data classification.

Keywords: 

Uncertain data, auto-encoder neural network, hyper-ellipsoid support vector machine, sparse coding

1. Introduction
2. Interval Uncertain Support Vector Machine
3. Sparse De-Noising Auto-Encoder Neural Network
4. Model of Sparse De-Noising Auto-Encoder Neural Network Classifier
5. Simulation Experiment
6. Conclusion
Acknowledgement
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