Learning from the crowd with neural network

Jingjing Li, Victor S. Sheng, Zhenyu Shu, Yanxia Cheng, Yuqin Jin, Yuan Feng Yan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In general, the first step for supervised learning from crowdsourced data is integration. To obtain training data as traditional machine learning, the ground truth for each example in the crowdsourcing dataset must be integrated with consensus algorithms. However, some information and correlations among labels in the crowdsourcing dataset have discarded after integration. In order to study whether the information and correlations are useful for learning, we proposed three types of neural networks. Experimental results show that i) all the three types of neural networks have abilities to predict labels for future unseen examples, ii) when labelers have lower qualities, the information and correlations in crowdsourcing datasets, which are discarded by integration, does improve the performance of neural networks significantly, iii) when labelers have higher label qualities, the information and correlations have little impact on improving accuracy of neural networks.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages693-698
Number of pages6
ISBN (Electronic)9781509002870
DOIs
StatePublished - Mar 2 2016
EventIEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 - Miami, United States
Duration: Dec 9 2015Dec 11 2015

Publication series

NameProceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015

Conference

ConferenceIEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
CountryUnited States
CityMiami
Period12/9/1512/11/15

Keywords

  • Crowdsourcing
  • Machine learning
  • Neural network

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