When in Doubt ... Be Indecisive

Authors:
Linda C. Gaag;Silja Renooij;Wilma Steeneveld;Henk Hogeveen
Affiliations:
Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands 3508 TB;Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands 3508 TB;Department of Farm Animal Health, Utrecht University,;Department of Farm Animal Health, Utrecht University,
Venue:
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2009

Citing 4
Cited 1

Bayesian Network Classifiers

Machine Learning - Special issue on learning with probabilistic representations
Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS

Journal of Biomedical Informatics - Special issue: Clinical machine learning
Diagnosing scrapie in sheep: A classification experiment

Computers in Biology and Medicine
Building classifiers using Bayesian networks

AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2

Sensor measurements revealed: Predicting the Gram-status of clinical mastitis causal pathogens

Computers and Electronics in Agriculture

Quantified Score

Hi-index	0.00

Visualization

Abstract

For a presented case, a Bayesian network classifier in essence computes a posterior probability distribution over its class variable. Based upon this distribution, the classifier's classification function returns a single, determinate class value and thereby hides the uncertainty involved. To provide reliable decision support, however, the classifier should be able to convey indecisiveness if the posterior distribution computed for the case does not clearly favour one class value over another. In this paper we present an approach for this purpose, and introduce new measures to capture the performance and practicability of such classifiers.