Keyword

label aggregation

Description: The process of combining multiple labels or annotations, often from different models or crowd workers, into a consolidated label.

Primary topic: Mobile Crowdsensing and Crowdsourcing (41%)

OpenAlex ID: keywords/label-aggregation

Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise

2009 · Jacob Whitehill, Tingfan Wu, et al. · 1,007

The multidimensional wisdom of crowds

2010 · Peter Welinder, Steve Branson, et al. · CaltechAUTHORS (California Institute of Technology) · 734

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Variational Inference for Crowdsourcing

2012 · Qiang Liu, Jian Peng, et al. · 297

Community-based bayesian aggregation models for crowdsourcing

2014 · Matteo Venanzi, John Guiver, et al. · International World Wide Web Conference (WWW) · 247

Adaptive Task Assignment for Crowdsourced Classification

2013 · Chien-Ju Ho, Shahin Jabbari, et al. · 208

Deep Learning from Crowds

2018 · Rodrigues, Filipe, Pereira, Francisco · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · 191

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Towards Class-Imbalance Aware Multi-Label Learning

2020 · Min-Ling Zhang, Yu-Kun Li, et al. · IEEE Transactions on Cybernetics · 170

Learning from Disagreement: A Survey

2021 · Alexandra N. Uma, Tommaso Fornaciari, et al. · Journal of Artificial Intelligence Research · 146

Max-Margin Majority Voting for Learning from Crowds

2018 · Tian Tian, Jun Zhu, et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 131

Multi-Class Ground Truth Inference in Crowdsourcing with Clustering

2015 · Jing Zhang, Victor S. Sheng, et al. · IEEE Transactions on Knowledge and Data Engineering · 114

Domain-Weighted Majority Voting for Crowdsourcing

2018 · Dapeng Tao, Jun Cheng, et al. · IEEE Transactions on Neural Networks and Learning Systems · 106

On the choice of spatial and categorical scale in remote sensing land cover classification

2005 · Junchang Ju, Sucharita Gopal, et al. · Remote Sensing of Environment · 104

ML-FOREST: A Multi-Label Tree Ensemble Method for Multi-Label Classification

2016 · Qingyao Wu, Mingkui Tan, et al. · IEEE Transactions on Knowledge and Data Engineering · 100

Learning From Crowds With Multiple Noisy Label Distribution Propagation

2021 · Liangxiao Jiang, Hao Zhang, et al. · IEEE Transactions on Neural Networks and Learning Systems · 93

Aggregating and Predicting Sequence Labels from Crowd Annotations

2017 · An Thanh Nguyen, Byron Wallace, et al. · Annual Meeting of the Association for Computational Linguistics (ACL) · 88

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On Releasing Annotator-Level Labels and Information in Datasets

2021 · Vinodkumar Prabhakaran, Aida Mostafazadeh Davani, et al. · 77

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On Aggregating Labels from Multiple Crowd Workers to Infer Relevance of Documents

2012 · Mehdi Hosseini, Ingemar J. Cox, et al. · Lecture notes in computer science · 65

Aggregating Crowd Wisdoms with Label-aware Autoencoders

2017 · Li'ang Yin, Jianhua Han, et al. · International Joint Conference on Artificial Intelligence · 61

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Noise filtering to improve data and model quality for crowdsourcing

2016 · Chaoqun Li, Victor S. Sheng, et al. · Knowledge-Based Systems · 60

Error Rate Bounds and Iterative Weighted Majority Voting for Crowdsourcing

2014 · Li, Hongwei, Yu, Bin · arXiv (Cornell University) · 57

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2020 · Fangna Tao, Liangxiao Jiang, et al. · Knowledge and Information Systems · 56

Robust Semi-Supervised Learning through Label Aggregation

2016 · Yan Yan, Zhongwen Xu, et al. · Proceedings of the AAAI Conference on Artificial Intelligence · 53

Multi-Objective Ranking Optimization for Product Search Using Stochastic Label Aggregation

2020 · David Carmel, Elad Haramaty, et al. · 52

Reputation-based Worker Filtering in Crowdsourcing

2014 · Srikanth Jagabathula, Lakshminarayanan Subramanian, et al. · neural information processing systems · 52

Accurate integration of crowdsourced labels using workers' self-reported confidence scores

2013 · Satoshi Oyama, Yukino Baba, et al. · International Joint Conference on Artificial Intelligence · 49

Works count: 523

Citation count: 7,310