A model-free subdata selection method for classification

arXiv:2404.19127v1 Announce Type: cross
Abstract: Subdata selection is a study of methods that select a small representative sample of the big data, the analysis of which is fast and statistically efficient. The existing subdata selection methods assume that the big data can be reasonably modeled using an underlying model, such as a (multinomial) logistic regression for classification problems. These methods work extremely well when the underlying modeling assumption is correct but often yield poor results otherwise. In this paper, we …

abstract analysis arxiv big big data classification data free multinomial sample small stat.me stat.ml study type