Learning from Crowds with Dual-View K-Nearest Neighbor

Jiao Li, Liangxiao Jiang, Xue Wu, Wenjun Zhang
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, PMLR 244:2238-2249, 2024.

Abstract

In crowdsourcing scenarios, we can obtain multiple noisy labels from different crowd workers for each instance and then infer its integrated label via label integration. To achieve better performance, some recently published label integration methods have attempted to exploit the multiple noisy labels of inferred instances’ nearest neighbors via the K-nearest neighbor (KNN) algorithm. However, the used KNN algorithm searches inferred instances’ nearest neighbors only relying on the defined distance functions in the original attribute view and totally ignoring the valuable information hidden in the multiple noisy labels, which limits their performance. Motivated by multi-view learning, we define the multiple noisy labels as another label view of instances and propose to search inferred instances’ nearest neighbors using the joint information from both the original attribute view and the multiple noisy label view. To this end, we propose a novel label integration method called dual-view K-nearest neighbor (DVKNN). In DVKNN, we first define a new distance function to search the K-nearest neighbors of an inferred instance. Then, we define a fine-grained weight for each noisy label from each neighbor. Finally, we perform weighted majority voting (WMV) on all these noisy labels to obtain the integrated label of the inferred instance. Extensive experiments validate the effectiveness and rationality of DVKNN.

Cite this Paper

BibTeX
@InProceedings{pmlr-v244-li24b, title = {Learning from Crowds with Dual-View K-Nearest Neighbor}, author = {Li, Jiao and Jiang, Liangxiao and Wu, Xue and Zhang, Wenjun}, booktitle = {Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence}, pages = {2238--2249}, year = {2024}, editor = {Kiyavash, Negar and Mooij, Joris M.}, volume = {244}, series = {Proceedings of Machine Learning Research}, month = {15--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v244/main/assets/li24b/li24b.pdf}, url = {https://proceedings.mlr.press/v244/li24b.html}, abstract = {In crowdsourcing scenarios, we can obtain multiple noisy labels from different crowd workers for each instance and then infer its integrated label via label integration. To achieve better performance, some recently published label integration methods have attempted to exploit the multiple noisy labels of inferred instances’ nearest neighbors via the K-nearest neighbor (KNN) algorithm. However, the used KNN algorithm searches inferred instances’ nearest neighbors only relying on the defined distance functions in the original attribute view and totally ignoring the valuable information hidden in the multiple noisy labels, which limits their performance. Motivated by multi-view learning, we define the multiple noisy labels as another label view of instances and propose to search inferred instances’ nearest neighbors using the joint information from both the original attribute view and the multiple noisy label view. To this end, we propose a novel label integration method called dual-view K-nearest neighbor (DVKNN). In DVKNN, we first define a new distance function to search the K-nearest neighbors of an inferred instance. Then, we define a fine-grained weight for each noisy label from each neighbor. Finally, we perform weighted majority voting (WMV) on all these noisy labels to obtain the integrated label of the inferred instance. Extensive experiments validate the effectiveness and rationality of DVKNN.} }
Endnote
%0 Conference Paper %T Learning from Crowds with Dual-View K-Nearest Neighbor %A Jiao Li %A Liangxiao Jiang %A Xue Wu %A Wenjun Zhang %B Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2024 %E Negar Kiyavash %E Joris M. Mooij %F pmlr-v244-li24b %I PMLR %P 2238--2249 %U https://proceedings.mlr.press/v244/li24b.html %V 244 %X In crowdsourcing scenarios, we can obtain multiple noisy labels from different crowd workers for each instance and then infer its integrated label via label integration. To achieve better performance, some recently published label integration methods have attempted to exploit the multiple noisy labels of inferred instances’ nearest neighbors via the K-nearest neighbor (KNN) algorithm. However, the used KNN algorithm searches inferred instances’ nearest neighbors only relying on the defined distance functions in the original attribute view and totally ignoring the valuable information hidden in the multiple noisy labels, which limits their performance. Motivated by multi-view learning, we define the multiple noisy labels as another label view of instances and propose to search inferred instances’ nearest neighbors using the joint information from both the original attribute view and the multiple noisy label view. To this end, we propose a novel label integration method called dual-view K-nearest neighbor (DVKNN). In DVKNN, we first define a new distance function to search the K-nearest neighbors of an inferred instance. Then, we define a fine-grained weight for each noisy label from each neighbor. Finally, we perform weighted majority voting (WMV) on all these noisy labels to obtain the integrated label of the inferred instance. Extensive experiments validate the effectiveness and rationality of DVKNN.
APA
Li, J., Jiang, L., Wu, X. & Zhang, W.. (2024). Learning from Crowds with Dual-View K-Nearest Neighbor. Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 244:2238-2249 Available from https://proceedings.mlr.press/v244/li24b.html.

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