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An Ordered Sample Consensus (ORSAC) Method for Data Cleaning Inspired by RANSAC: Identifying Probable Mislabeled Data
  • Thomas Jenkins ,
  • Sameerah Talafha ,
  • Adam Goodwin
Thomas Jenkins

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Sameerah Talafha
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Adam Goodwin
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In classification problems, mislabeled data can have a dramatic effect on the capability of a trained model. The traditional method of dealing with mislabeled data is through expert review of the data. However, this is not always ideal, due to both the large volume of data in many classification datasets, as with image datasets supporting deep learning models, and the limited availability of human experts for review of the data. Herein we propose an Ordered Sample Consensus (ORSAC) method to support data cleaning by flagging mislabeled data. This method is inspired by the Random Sample Consensus (RANSAC) method for outlier detection. In short, the method involves iteratively training and testing a model on different splits of the dataset, recording misclassifications, and flagging data which is frequently misclassified as probable mislabels. We evaluate the method by purposefully mislabeling subsets of the data and assessing the method’s capabilities to find such data. We demonstrate with three datasets, a mosquito image dataset, CIFAR-10, and CIFAR-100, that this method is reliable in finding mislabeled data with a high degree of accuracy. Our experimental results indicate a high proficiency of our methodology in identifying mislabeled data across these diverse datasets, with performance assessed using different mislabeling frequencies.