A Label Noise Robust Stacked Auto-Encoder Algorithm for Inaccurate Supervised Classification Problems
Joint Authors
Wang, Zi-yang
Luo, Xiao-yi
Liang, Jun
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-19, 19 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-05-14
Country of Publication
Egypt
No. of Pages
19
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Zi-yang& Luo, Xiao-yi& Liang, Jun. 2019. A Label Noise Robust Stacked Auto-Encoder Algorithm for Inaccurate Supervised Classification Problems. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-19.
https://search.emarefa.net/detail/BIM-1194680
Modern Language Association (MLA)
Wang, Zi-yang…[et al.]. A Label Noise Robust Stacked Auto-Encoder Algorithm for Inaccurate Supervised Classification Problems. Mathematical Problems in Engineering No. 2019 (2019), pp.1-19.
https://search.emarefa.net/detail/BIM-1194680
American Medical Association (AMA)
Wang, Zi-yang& Luo, Xiao-yi& Liang, Jun. A Label Noise Robust Stacked Auto-Encoder Algorithm for Inaccurate Supervised Classification Problems. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-19.
https://search.emarefa.net/detail/BIM-1194680
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1194680