Training Set Debugging Using Trusted Items
This work addresses data quality issues in machine learning for practitioners dealing with large, noisy datasets, though it is incremental as it builds on existing optimization methods for data debugging.
The paper tackles the problem of identifying bugs in large training datasets by using a small set of verified trusted items, proposing an algorithm that finds the minimal label changes needed for the model to correctly predict these items, with experiments showing effective bug identification and label correction.
Training set bugs are flaws in the data that adversely affect machine learning. The training set is usually too large for man- ual inspection, but one may have the resources to verify a few trusted items. The set of trusted items may not by itself be adequate for learning, so we propose an algorithm that uses these items to identify bugs in the training set and thus im- proves learning. Specifically, our approach seeks the smallest set of changes to the training set labels such that the model learned from this corrected training set predicts labels of the trusted items correctly. We flag the items whose labels are changed as potential bugs, whose labels can be checked for veracity by human experts. To find the bugs in this way is a challenging combinatorial bilevel optimization problem, but it can be relaxed into a continuous optimization problem. Ex- periments on toy and real data demonstrate that our approach can identify training set bugs effectively and suggest appro- priate changes to the labels. Our algorithm is a step toward trustworthy machine learning.