Modeling for Noisy Labels of Crowd Workers

2016 ◽  
pp. 227-238 ◽  
Author(s):  
Qian Yan ◽  
Hao Huang ◽  
Yunjun Gao ◽  
Chen Ying ◽  
Qingyang Hu ◽  
...  
Keyword(s):  
Noisy Labels

scholarly journals Drift Compensation on Massive Online Electronic-Nose Responses

Chemosensors ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 78
Author(s):  
Jianhua Cao ◽  
Tao Liu ◽  
Jianjun Chen ◽  
Tao Yang ◽  
Xiuxiu Zhu ◽  
...  
Keyword(s):  
Active Learning ◽  
Gas Sensor ◽  
Electronic Nose ◽  
Effective Means ◽  
Learning Paradigm ◽  
Class Label ◽  
Learning Framework ◽  
Sensor Drift ◽  
Drift Compensation ◽  
Noisy Labels

Gas sensor drift is an important issue of electronic nose (E-nose) systems. This study follows this concern under the condition that requires an instant drift compensation with massive online E-nose responses. Recently, an active learning paradigm has been introduced to such condition. However, it does not consider the “noisy label” problem caused by the unreliability of its labeling process in real applications. Thus, we have proposed a class-label appraisal methodology and associated active learning framework to assess and correct the noisy labels. To evaluate the performance of the proposed methodologies, we used the datasets from two E-nose systems. The experimental results show that the proposed methodology helps the E-noses achieve higher accuracy with lower computation than the reference methods do. Finally, we can conclude that the proposed class-label appraisal mechanism is an effective means of enhancing the robustness of active learning-based E-nose drift compensation.

scholarly journals From Street Photos to Fashion Trends: Leveraging User-Provided Noisy Labels for Fashion Understanding

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 49189-49205
Author(s):  
Fu-Hsien Huang ◽  
Hsin-Min Lu ◽  
Yao-Wen Hsu
Keyword(s):  
Noisy Labels

A fault diagnosis method for wind turbines gearbox based on adaptive loss weighted meta-ResNet under noisy labels

2021 ◽  
Vol 161 ◽  
pp. 107963
Author(s):  
Kai Zhang ◽  
Baoping Tang ◽  
Lei Deng ◽  
Qian Tan ◽  
Haoshuai Yu
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbines ◽  
Diagnosis Method ◽  
Noisy Labels

scholarly journals Learning from Noisy Labels with Distillation

2017 ◽  
Author(s):  
Yuncheng Li ◽  
Jianchao Yang ◽  
Yale Song ◽  
Liangliang Cao ◽  
Jiebo Luo ◽  
...  
Keyword(s):  
Noisy Labels

scholarly journals EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels

2021 ◽  
Author(s):  
Ragav Sachdeva ◽  
Filipe R. Cordeiro ◽  
Vasileios Belagiannis ◽  
Ian Reid ◽  
Gustavo Carneiro
Keyword(s):  
Closed Set ◽  
Open Set ◽  
Noisy Labels

Learning Cross-Modal Retrieval with Noisy Labels

2021 ◽  
Author(s):  
Peng Hu ◽  
Xi Peng ◽  
Hongyuan Zhu ◽  
Liangli Zhen ◽  
Jie Lin
Keyword(s):  
Noisy Labels

TC-Net: Detecting Noisy Labels via Transform Consistency

2021 ◽  
pp. 1-1
Author(s):  
Rumeng Yi ◽  
Yaping Huang
Keyword(s):  
Noisy Labels

scholarly journals Agreeing to disagree: active learning with noisy labels without crowdsourcing

2017 ◽  
Vol 9 (8) ◽  
pp. 1307-1319 ◽  
Author(s):  
Mohamed-Rafik Bouguelia ◽  
Slawomir Nowaczyk ◽  
K. C. Santosh ◽  
Antanas Verikas
Keyword(s):  
Active Learning ◽  
Agreeing To Disagree ◽  
Noisy Labels

scholarly journals Automated mapping of laboratory tests to LOINC codes using noisy labels in a national electronic health record system database

2018 ◽  
Vol 25 (10) ◽  
pp. 1292-1300 ◽  
Author(s):  
Sharidan K Parr ◽  
Matthew S Shotwell ◽  
Alvin D Jeffery ◽  
Thomas A Lasko ◽  
Michael E Matheny
Keyword(s):  
Machine Learning ◽  
Laboratory Tests ◽  
Laboratory Data ◽  
Unlabeled Data ◽  
Department Of Veterans Affairs ◽  
Test Frequency ◽  
Record System ◽  
Automated Mapping ◽  
Meaningful Context ◽  
Noisy Labels

Abstract Objective Standards such as the Logical Observation Identifiers Names and Codes (LOINC®) are critical for interoperability and integrating data into common data models, but are inconsistently used. Without consistent mapping to standards, clinical data cannot be harmonized, shared, or interpreted in a meaningful context. We sought to develop an automated machine learning pipeline that leverages noisy labels to map laboratory data to LOINC codes. Materials and Methods Across 130 sites in the Department of Veterans Affairs Corporate Data Warehouse, we selected the 150 most commonly used laboratory tests with numeric results per site from 2000 through 2016. Using source data text and numeric fields, we developed a machine learning model and manually validated random samples from both labeled and unlabeled datasets. Results The raw laboratory data consisted of >6.5 billion test results, with 2215 distinct LOINC codes. The model predicted the correct LOINC code in 85% of the unlabeled data and 96% of the labeled data by test frequency. In the subset of labeled data where the original and model-predicted LOINC codes disagreed, the model-predicted LOINC code was correct in 83% of the data by test frequency. Conclusion Using a completely automated process, we are able to assign LOINC codes to unlabeled data with high accuracy. When the model-predicted LOINC code differed from the original LOINC code, the model prediction was correct in the vast majority of cases. This scalable, automated algorithm may improve data quality and interoperability, while substantially reducing the manual effort currently needed to accurately map laboratory data.

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