Work

The optimal crowd learning machine

Year: 2017

Type: article

Abstract: BACKGROUND: Any family of learning machines can be combined into a single learning machine using various methods with myriad degrees of usefulness. RESULTS: For making predictions on an outcome, it is provably at least as good as the best machine in the family, given sufficient data. And if one machine in the family minimizes the probability of misclassification, in the limit of large data, then Optimal Crowd does also. That is, the Optimal Crowd is asymptotically Bayes optimal if any machine in the crowd is such. CONCLUSIONS: The only assumption needed for proving optimality is that the outcome variable is bounded. The scheme is illustrated using real-world data from the UCI machine learning site, and possible extensions are proposed.

Source: BioData Mining

DOI: 10.1186/s13040-017-0135-7

Authors: Bilguunzaya Battogtokh, Majid Mojirsheibani, James Malley

Institutions: National Institutes of Health, Center for Information Technology, California State University, Northridge

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,690

Funder: National Institutes of Health