A Label Noise Robust Stacked Auto-Encoder Algorithm for Inaccurate Supervised Classification Problems

In real applications, label noise and feature noise are two main noise sources. Similar to feature noise, label noise imposes great detriment on training classification models. Motivated by successful application of deep learning method in normal classification problems, this paper proposes a new fr...

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Bibliographic Details
Published in:Mathematical problems in engineering 2019-01, Vol.2019 (2019), p.1-19
Main Authors: Wang, Zi-yang, Liang, Jun, Luo, Xiao-yi
Format: Article
Language:English
Subjects:
Algorithms
Classification
Datasets
Machine learning
Noise
Noise reduction
Representations
Robustness
Training
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Summary:In real applications, label noise and feature noise are two main noise sources. Similar to feature noise, label noise imposes great detriment on training classification models. Motivated by successful application of deep learning method in normal classification problems, this paper proposes a new framework called LNC-SDAE to handle those datasets corrupted with label noise, or so-called inaccurate supervision problems. The LNC-SDAE framework contains a preliminary label noise cleansing part and a stacked denoising auto-encoder. In preliminary label noise cleansing part, the K-fold cross-validation thought is applied for detecting and relabeling those mislabeled samples. After being preprocessed by label noise cleansing part, the cleansed training dataset is then input into the stacked denoising auto-encoder to learn robust representation for classification. A corrupted UCI standard dataset and a corrupted real industrial dataset are used for test, both of which contain a certain proportion of label noise (the ratio changes from 0% to 30%). The experiment results prove the effectiveness of LNC-SDAE, the representation learnt by which is shown robust.
ISSN:1024-123X
1563-5147
DOI:10.1155/2019/2182616