Work

Unbiased Estimations based on Binary Classifiers: A Maximum Likelihood Approach

Year: 2021

Type: preprint

Abstract: Binary classifiers trained on a certain proportion of positive items introduce a bias when applied to data sets with different proportions of positive items. Most solutions for dealing with this issue assume that some information on the latter distribution is known. However, this is not always the case, certainly when this proportion is the target variable. In this paper a maximum likelihood estimator for the true proportion of positives in data sets is suggested and tested on synthetic and real world data.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2102.08659

Authors: Puts, Marco J. H., Daas, Piet J. H.

Institutions: Association for Computing Machinery, Eindhoven University of Technology

Language: English

Topic: Imbalanced Data Classification Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3131716367

Cites: 3

Cited by: 1

Related to: 20