Guess-and-Verify Heuristics for Reducing Uncertainties in Expert Classification Systems
Abstract
An expert classification system having statistical information about the prior probabilities of the different classes should be able to use this knowledge to reduce the amount of additional information that it must collect, e.g., through questions, in order to make a correct classification. This paper examines how best to use such prior information and additional information-collection opportunities to reduce uncertainty about the class to which a case belongs, thus minimizing the average cost or effort required to correctly classify new cases.
Cite
Text
Qiu et al. "Guess-and-Verify Heuristics for Reducing Uncertainties in Expert Classification Systems." Conference on Uncertainty in Artificial Intelligence, 1992. doi:10.1016/B978-1-4832-8287-9.50039-6Markdown
[Qiu et al. "Guess-and-Verify Heuristics for Reducing Uncertainties in Expert Classification Systems." Conference on Uncertainty in Artificial Intelligence, 1992.](https://mlanthology.org/uai/1992/qiu1992uai-guess/) doi:10.1016/B978-1-4832-8287-9.50039-6BibTeX
@inproceedings{qiu1992uai-guess,
title = {{Guess-and-Verify Heuristics for Reducing Uncertainties in Expert Classification Systems}},
author = {Qiu, Yuping and Jr., Louis Anthony Cox and Davis, Lawrence},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1992},
pages = {252-258},
doi = {10.1016/B978-1-4832-8287-9.50039-6},
url = {https://mlanthology.org/uai/1992/qiu1992uai-guess/}
}