Abstract
The performance of classification tasks extremely relies on data quality, while in real world label noises inevitably exists because of data entry errors, transmit errors and subjectivity of taggers. Different methods have been proposed to deal with label imperfection, including robust algorithms by avoid overfitting, filtering mechanism to remove noises and correction mechanism to revise noises. In this paper, we propose an approach based on knowledge graph to perceive and correct the label errors in training data. Experiments on a medical Q&A data set reveal that our knowledge graph based approach can be effective on promoting classification performance and data quality. The results as well show our approach can work in a relatively high noise level and be applied in other data mining tasks demanding deep understanding.
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Acknowledgements
This work was supported by the NSFC (No. 61272099, 61261160502 and 61202025), Shanghai Excellent Academic Leaders Plan (No. 11XD1402900), the Program for Changjiang Scholars and Innovative Research Team in University of China (IRT1158, PCSIRT), the Scientific Innovation Act of STCSM (No. 13511504200), Singapore NRF (CREATE E2S2), and the EU FP7 CLIMBER project (No. PIRSES-GA-2012-318939).
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Liu, Y., Li, H., Chen, Y. (2014). Using Knowledge Graph to Handle Label Imperfection. In: Peng, WC., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8643. Springer, Cham. https://doi.org/10.1007/978-3-319-13186-3_32
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DOI: https://doi.org/10.1007/978-3-319-13186-3_32
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