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Label distribution‐based noise correction for multiclass crowdsourcing
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/int.21567openalex-snapshot
Image Classification Based on the Combination of Text Features and Visual Features
inferredcites →https://doi.org/10.1002/int.22291openalex-snapshot
Cross lingual speech emotion recognition via triple attentive asymmetric convolutional neural network
inferredcites →https://doi.org/10.1002/int.22529openalex-snapshot
Hybrid sequence‐based Android malware detection using natural language processing
inferredcites →https://doi.org/10.1007/s10115-020-01475-yopenalex-snapshot
Label similarity-based weighted soft majority voting and pairing for crowdsourcing
inferredcites →https://doi.org/10.1016/j.engappai.2019.04.004openalex-snapshot
Noise correction to improve data and model quality for crowdsourcing
inferredcites →https://doi.org/10.1016/j.eswa.2016.09.003openalex-snapshot
Label noise correction and application in crowdsourcing
inferredcites →https://doi.org/10.1016/j.ins.2019.08.071openalex-snapshot
Class-specific attribute value weighting for Naive Bayes
inferredcites →https://doi.org/10.1016/j.ins.2020.08.117openalex-snapshot
Improving data and model quality in crowdsourcing using cross-entropy-based noise correction
inferredcites →https://doi.org/10.1016/j.knosys.2016.06.003openalex-snapshot
Noise filtering to improve data and model quality for crowdsourcing
inferredcites →https://doi.org/10.1016/j.patcog.2018.11.032openalex-snapshot
Class-specific attribute weighted naive Bayes
inferredcites →https://doi.org/10.1023/a:1009715923555openalex-snapshot
A Tutorial on Support Vector Machines for Pattern Recognition
inferredcites →https://doi.org/10.1080/21642583.2017.1294118openalex-snapshot
Adaptive noise risk modelling: fuzzy logic approach
inferredcites →https://doi.org/10.1109/tkde.2015.2504974openalex-snapshot
Multi-Class Ground Truth Inference in Crowdsourcing with Clustering
inferredcites →https://doi.org/10.1109/tkde.2017.2659740openalex-snapshot
Majority Voting and Pairing with Multiple Noisy Labeling
inferredcites →https://doi.org/10.1109/tkde.2018.2836440openalex-snapshot
A Correlation-Based Feature Weighting Filter for Naive Bayes
inferredcites →https://doi.org/10.1109/tnnls.2017.2677468openalex-snapshot
Improving Crowdsourced Label Quality Using Noise Correction
inferredcites →https://doi.org/10.1109/tnnls.2021.3082496openalex-snapshot
Learning From Crowds With Multiple Noisy Label Distribution Propagation
inferredcites →https://doi.org/10.1109/tpami.2017.2648786openalex-snapshot
Learning Supervised Topic Models for Classification and Regression from Crowds
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-981-99-4752-2_24openalex-snapshot
Instance Weighting-Based Noise Correction for Crowdsourcing
inferred← citeshttps://doi.org/10.1007/s11704-022-2225-zopenalex-snapshot
Attribute augmentation-based label integration for crowdsourcing
inferred← citeshttps://doi.org/10.1007/s11704-023-2751-3openalex-snapshot
Label distribution similarity-based noise correction for crowdsourcing
inferred← citeshttps://doi.org/10.1007/s13042-025-02546-8openalex-snapshot
The processing for label noise based on attribute reduction and two-step method
inferred← citeshttps://doi.org/10.1016/j.engappai.2024.108439openalex-snapshot
Label noise correction for crowdsourcing using dynamic resampling
inferred← citeshttps://doi.org/10.1016/j.ijar.2023.108973openalex-snapshot
Three-way decision-based noise correction for crowdsourcing
inferred← citeshttps://doi.org/10.1016/j.is.2023.102321openalex-snapshot
Worker similarity-based noise correction for crowdsourcing
inferred← citeshttps://doi.org/10.1016/j.patcog.2024.110325openalex-snapshot
Certainty weighted voting-based noise correction for crowdsourcing
inferred← citeshttps://doi.org/10.1088/1361-6579/acb8feopenalex-snapshot
Improved Parkinsonian tremor quantification based on automatic label modification and SVM with RBF kernel
inferred← citeshttps://doi.org/10.1109/access.2024.3432729openalex-snapshot
Boosting Crowdsourced Annotation Accuracy: Small Loss Filtering and Augmentation-Driven Training
inferred← citeshttps://doi.org/10.1109/icdm59182.2024.00061openalex-snapshot
Probabilistic Matrix Factorization-based Three-stage Label Completion for Crowdsourcing
inferred← citeshttps://doi.org/10.1109/jiot.2023.3244547openalex-snapshot
Dual-View Noise Correction for Crowdsourcing
inferred← citeshttps://doi.org/10.1109/tbdata.2024.3426310openalex-snapshot
Worker Similarity-Based Label Completion for Crowdsourcing
inferred← citeshttps://doi.org/10.1109/tetci.2025.3551982openalex-snapshot
Learning From Crowds by Class-Specific Instance Weighting
inferred← citeshttps://doi.org/10.1145/3591363openalex-snapshot
Noise-aware Local Model Training Mechanism for Federated Learning
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