Abstract
Existing supervised methods for error detection require access to clean labels in order to train the classification models. This is difficult to achieve in practical scenarios. While the majority of the error detection algorithms ignore the effect of noisy labels, in this paper, we design effective techniques for error detection when both data and labels contain noise. Nevertheless, we present TabMentor, a novel deep-learning model for error detection on tabular data with noisy training labels. TabMentor introduces a deep model for the prediction, i.e., Tabclassifier that suggests the most salient features for the decision step, enabling efficient learning. For feature extraction, it uses existing error detection algorithms, along with some raw features from the datasets. To reduce the negative effect of noisy training labels on the model, TabMentor uses another deep model, i.e., Teachernet, to supervise the training of Tabclassifier. During the training process, both Teachernet and Tabclassifier dynamically learn curriculum from data, allowing Tabclassifier to focus more on clean labeled samples. Performance evaluation using five different data sets shows that the TabMentor excels over the best baseline error detection system by 0.05 to 0.11 in terms of F1 scores.
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Acknowledgements
National Key R &D program of China 2021YFB3301500, Guangdong Provincial Natural Science Foundation 2019A1515111047, Shenzhen Colleges and Universities Continuous Support Grant 20200811104054002, Guangdong “Pearl River Talent Recruitment Program” under Grant 2019ZT08X603, the 14th “115” Industrial Innovation Group (Project 4) of Anhui Province, NSFC 62072311, U2001212, Guangdong Project 2020B1515120028, and Shenzhen Project JCYJ20210324094402008.
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Zhang, Y., Qin, J., Wang, Y., Ali, M.A., Ji, Y., Mao, R. (2023). TabMentor: Detect Errors on Tabular Data with Noisy Labels. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14178. Springer, Cham. https://doi.org/10.1007/978-3-031-46671-7_12
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