Cleaning Crowdsourced Labels Using Oracles For Statistical Classification
By Mohamad Dolatshah, Mathew Teoh, Jiannan Wang, Jian Pei. 2018
39 citations recorded by the source corpus.
Nowadays, crowdsourcing is being widely used to collect training data for solving classification problems. However, crowdsourced labels are often noisy, and there is a performance gap between classification with noisy labels and classification with ground-truth labels. In this paper, we consider how to apply oracle-based label cleaning to reduce the gap. We propose TARS, a label-cleaning advisor that can provide two pieces of valuable advice for data scientists when they need to train or test a model using noisy labels. Firstly, in the model testing stage, given a test dataset with noisy labels, and a classification model, TARS can use the test data to estimate how well the model will perform w.r.t. ground-truth labels. Secondly, in the model training stage, given a training dataset with noisy labels, and a classification algorithm, TARS can determine which label should be sent to an oracle to clean such that the model can be improved the most. For the first advice, we propose an effective estimation technique, and study how to compute confidence intervals to bound its estimation error. For the second advice, we propose a novel cleaning strategy along with two optimization techniques, and illustrate that it is superior to the existing cleaning strategies. We evaluate TARS on both simulated and real-world datasets. The results show that (1) TARS can use noisy test data to accurately estimate a model's true performance for various evaluation metrics; and (2) TARS can improve the model accuracy by a larger margin than the existing cleaning strategies, for the same cleaning budget.
Subfields: Artificial Intelligence, Computer Science Applications
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Citations in the corpus
- A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, neutral): “Recently, TARS [129] was proposed to solve the problem of cleaning crowdsourced labels using oracles.”
- Data collection and quality challenges in deep learning: a data-centric AI perspective (2021, background): “Hence, TARS can selectively clean labels that are expected to benefit model accuracy the most.”
- PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models (2020, background): “The goal of data cleaning is to detect and fix errors in data, and is a crucial step in preparing data for data analytics/machine learning tasks [13, 33].”