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Interactive correction of mislabeled training data

Xiang, Shouxing, Ye, Xi, Xia, Jiazhi, Wu, Jing ORCID:, Chen, Yang and Liu, Shixia 2019. Interactive correction of mislabeled training data. Presented at: IEEE VIS 2019, Vancouver, BC, Canada, 20-25 October 2019. 2019 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, 10.1109/VAST47406.2019.8986943

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In this paper, we develop a visual analysis method for interactively improving the quality of labeled data, which is essential to the success of supervised and semi-supervised learning. The quality improvement is achieved through the use of user-selected trusted items. We employ a bi-level optimization model to accurately match the labels of the trusted items and to minimize the training loss. Based on this model, a scalable data correction algorithm is developed to handle tens of thousands of labeled data efficiently. The selection of the trusted items is facilitated by an incremental tSNE with improved computational efficiency and layout stability to ensure a smooth transition between different levels. We evaluated our method on real-world datasets through quantitative evaluation and case studies, and the results were generally favorable.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: IEEE
ISBN: 9781728122854
Date of First Compliant Deposit: 29 July 2019
Last Modified: 09 Dec 2022 10:03

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