Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits
Hiroyuki Namba Shota Horiguchi Masaki Hamamoto Masashi Egi
Data cleansing aims to improve model performance by removing a set of harmful
instances from the training dataset. Data Shapley is a common theoretically
guaranteed method to evaluate the contribution of each instance to model
performance; however, it requires training on all subsets of the training data,
which is computationally expensive. In this paper, we propose an
iterativemethod to fast identify a subset of instances with low data Shapley
values by using the thresholding bandit algorithm. We provide a theoretical
guarantee that the proposed method can accurately select harmful instances if a
sufficiently large number of iterations is conducted. Empirical evaluation
using various models and datasets demonstrated that the proposed method
efficiently improved the computational speed while maintaining the model
performance.